references.bib

@article{murphy1985role,
title={The role of theories in conceptual coherence.},
author={Murphy, Gregory L and Medin, Douglas L},
journal={Psychological review},
volume={92},
number={3},
pages={289},
year={1985},
publisher={American Psychological Association},
note={Early work highlighting the importance of structured conceptual theories, rather than similarity-based metrics, in knowledge representation (specifically categorization)}
}
@book{turner1994adaptive,
title={Adaptive reasoning for real-world problems: A schema-based approach},
author={Turner, Roy},
year={1994},
publisher={Psychology Press}
}
@article{rosenberg2013visual,
title={The visual representation of 3D object orientation in parietal cortex},
author={Rosenberg, Ari and Cowan, Noah J and Angelaki, Dora E},
journal={Journal of Neuroscience},
volume={33},
number={49},
pages={19352--19361},
year={2013},
publisher={Soc Neuroscience}
}
@article{kim2019type,
title={A Type-coherent, Expressive Representation as an Initial Step to Language Understanding},
author={Kim, Gene Louis and Schubert, Lenhart},
journal={arXiv preprint arXiv:1903.09333},
year={2019}
}
@incollection{cooper1973chronometric,
title={Chronometric studies of the rotation of mental images},
author={Cooper, Lynn A and Shepard, Roger N},
booktitle={Visual information processing},
pages={75--176},
year={1973},
publisher={Elsevier}
}
@inproceedings{platonov2018computational,
title={Computational Models for Spatial Prepositions},
author={Platonov, Georgiy and Schubert, Lenhart},
booktitle={Proceedings of the First International Workshop on Spatial Language Understanding},
pages={21--30},
year={2018}
}
@article{palmer2005proposition,
title={The proposition bank: An annotated corpus of semantic roles},
author={Palmer, Martha and Gildea, Daniel and Kingsbury, Paul},
journal={Computational linguistics},
volume={31},
number={1},
pages={71--106},
year={2005},
publisher={MIT Press}
}
@inproceedings{siskind2015,
author = {Siskind, Jeffrey Mark},
title = {Conducting Neuroscience to Guide the Development of AI},
booktitle = {Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence},
series = {AAAI'15},
year = {2015},
isbn = {0-262-51129-0},
location = {Austin, Texas},
pages = {4067--4072},
numpages = {6},
url = {http://dl.acm.org/citation.cfm?id=2888116.2888285},
acmid = {2888285},
publisher = {AAAI Press},
}
@inproceedings{kim2019FLAIRS,
title={Towards Parsing Unscoped Episodic Logical Forms with a Cache Transition Parser},
author={Gene Louis Kim},
booktitle={the Poster Abstracts of the Proceedings of the 32nd International Conference of the Florida Artificial Intelligence Research Society},
year={2019},
location={Sarasota, Florida, USA},
}
@incollection{turing1950,
author = {Turing, A. M.},
chapter = {Computing Machinery and Intelligence},
title = {Computers \& Thought},
editor = {Feigenbaum, Edward A. and Feldman, Julian},
year = {1950},
isbn = {0-262-56092-5},
pages = {11--35},
numpages = {25},
url = {http://dl.acm.org/citation.cfm?id=216408.216410},
acmid = {216410},
publisher = {MIT Press},
address = {Cambridge, MA, USA},
}
@phdthesis{mooney:phd88,
title={A General Explanation-Based Learning Mechanism and its Application to Narrative Understanding},
school={University of Illinois Urbana-Champaign},
author={Raymond J. Mooney},
institution={Department of Computer Science, University of Illinois at Urbana-Champaign},
url="http://www.cs.utexas.edu/users/ai-lab/?mooney:phd88",
year={1988}
}
@article{robinson1965machine,
title={A machine-oriented logic based on the resolution principle},
author={Robinson, John Alan},
journal={Journal of the ACM},
volume={12},
number={1},
pages={23--41},
year={1965}
}
@InCollection{schubert2000book2,
author = {Schubert, Lenhart K.},
title = {The Situations We Talk About},
booktitle = {Logic-based Artificial Intelligence},
editor = {Minker, Jack},
year = {2000},
pages = {407--439},
publisher = {Kluwer Academic Publishers},
address = {Norwell, MA, USA},
}
@book{schankandabelson,
title={Scripts, Plans, Goals, and Understanding: An Inquiry Into Human Knowledge Structures},
author={Schank, R.C. and Abelson, R.P.},
isbn={9780470990339},
lccn={lc76051963},
series={The Artificial Intelligence Series},
url={https://books.google.com/books?id=YZ99AAAAMAAJ},
year={1977},
publisher={Lawrence Erlbaum Associates}
}
@incollection{Reiter:1987:CWD:42641.42663,
author = {Reiter, R.},
title = {On Closed World Data Bases},
chapter = {5},
booktitle = {Readings in Nonmonotonic Reasoning},
editor = {Ginsberg, Matthew L.},
year = {1987},
isbn = {0-934613-45-1},
pages = {300--310},
numpages = {11},
url = {http://dl.acm.org/citation.cfm?id=42641.42663},
acmid = {42663},
publisher = {Morgan Kaufmann Publishers Inc.},
address = {San Francisco, CA, USA},
}
@article{allen1983,
author = {Allen, James F.},
title = {Maintaining Knowledge About Temporal Intervals},
journal = {Commun. ACM},
issue_date = {Nov. 1983},
volume = {26},
number = {11},
month = nov,
year = {1983},
issn = {0001-0782},
pages = {832--843},
numpages = {12},
url = {http://doi.acm.org/10.1145/182.358434},
doi = {10.1145/182.358434},
acmid = {358434},
publisher = {ACM},
address = {New York, NY, USA},
keywords = {interval reasoning, interval representation, temporal interval},
}
@book{bartlett1995remembering,
title={Remembering: A study in experimental and social psychology},
author={Bartlett, Frederic C},
volume={14},
year={1932},
publisher={Cambridge University Press}
}
@book{piaget1952origins,
title={The Origins of Intelligence in Children},
author={Piaget, Jean and Cook, Margaret},
volume={8},
year={1952},
publisher={International Universities Press New York}
}
@InCollection{mccarthy69,
author = "John McCarthy and Patrick J. Hayes",
title = "Some Philosophical Problems from the Standpoint of
Artificial Intelligence",
booktitle = "Machine Intelligence 4",
editor = "B. Meltzer and D. Michie",
pages = "463--502",
publisher = "Edinburgh University Press",
year = "1969",
note = "reprinted in McC90",
}
@article{bobrow1977overview,
title={An overview of KRL, a knowledge representation language},
author={Bobrow, Daniel G and Winograd, Terry},
journal={Cognitive science},
volume={1},
number={1},
pages={3--46},
year={1977},
publisher={Wiley Online Library}
}
@article{smith2007obo,
title={The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration},
author={Smith, Barry and Ashburner, Michael and Rosse, Cornelius and Bard, Jonathan and Bug, William and Ceusters, Werner and Goldberg, Louis J and Eilbeck, Karen and Ireland, Amelia and Mungall, Christopher J and others},
journal={Nature biotechnology},
volume={25},
number={11},
pages={1251},
year={2007},
publisher={Nature Publishing Group}
}
@article{fikes1972,
title={Learning and executing generalized robot plans},
author={Fikes, Richard E and Hart, Peter E and Nilsson, Nils J},
journal={Artificial intelligence},
volume={3},
pages={251--288},
year={1972},
publisher={Elsevier}
}
@article{mooney90,
title={Learning Plan Schemata From Observation: Explanation-Based Learning for Plan Recognition},
author={Mooney, Raymond J.},
volume={14},
journal={Cognitive Science},
number={4},
pages={483-509},
url="http://www.cs.utexas.edu/users/ai-lab/?mooney:cogsci90",
year={1990}
}
@article{habgood2018knowledge,
title={Knowledge-how, abilities, and questions},
author={Habgood-Coote, Joshua},
journal={Australasian Journal of Philosophy},
pages={1--19},
year={2018},
publisher={Taylor \& Francis},
note={First paper I found regarding formal semantics for knowledge-how}
}
@article{mitchell1986,
author = {Mitchell, Tom M. and Keller, Richard M. and Kedar-Cabelli, Smadar T.},
title = {Explanation-Based Generalization: A Unifying View},
journal = {Mach. Learn.},
volume = {1},
number = {1},
month = mar,
year = {1986},
issn = {0885-6125},
pages = {47--80},
numpages = {34},
url = {http://dx.doi.org/10.1023/A:1022691120807},
doi = {10.1023/A:1022691120807},
acmid = {637968},
publisher = {Kluwer Academic Publishers},
address = {Hingham, MA, USA},
keywords = {constraint back-propagation, explanation-based generalization, explanation-based learning, goal regression, operationalization, similarity-based generalization},
}
@inproceedings{mooneydejong1985,
author = {Mooney, Raymond and DeJong, Gerald},
title = {Learning Schemata for Natural Language Processing},
booktitle = {Proceedings of the 9th International Joint Conference on Artificial Intelligence - Volume 1},
series = {IJCAI'85},
year = {1985},
isbn = {0-934613-02-8},
location = {Los Angeles, California},
pages = {681--687},
numpages = {7},
url = {http://dl.acm.org/citation.cfm?id=1625135.1625267},
acmid = {1625267},
publisher = {Morgan Kaufmann Publishers Inc.},
address = {San Francisco, CA, USA},
}
@article{hwang1993episodic,
title={Episodic logic: A situational logic for natural language processing},
author={Hwang, Chung Hee and Schubert, Lenhart K},
journal={Situation Theory and its Applications},
volume={3},
pages={303--338},
year={1993},
publisher={Chicago University Press}
}
@incollection{Davidson1967,
title = {The Logical Form of Action Sentences},
year = {1967},
editor = {Nicholas Rescher},
author = {Donald Davidson},
booktitle = {The Logic of Decision and Action},
publisher = {University of Pittsburgh Press}
}
@inproceedings{P18-1001,
title = "Probabilistic FastText for Multi-Sense Word Embeddings",
author = "Athiwaratkun, Ben  and
Wilson, Andrew  and
Anandkumar, Anima",
booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = may,
year = "2019",
publisher = "Association for Computational Linguistics",
}
@article{liu2004conceptnet,
title={ConceptNet—a practical commonsense reasoning tool-kit},
author={Liu, Hugo and Singh, Push},
journal={BT technology journal},
volume={22},
number={4},
pages={211--226},
year={2004},
publisher={Springer}
}
@incollection{winograd1973procedural,
title={A procedural model of language understanding},
author={Winograd, Terry},
year={1973},
booktitle={Computer Models of Thought and Language},
pages={152--186},
editor={R Schank and K Colby},
publisher={New York, NY: WH Freeman},
}
@inproceedings{schubert1992tensetrees,
author = {Hwang, Chung Hee and Schubert, Lenhart K.},
title = {Tense Trees As the "Fine Structure" of Discourse},
booktitle = {Proceedings of the 30th Annual Meeting on Association for Computational Linguistics},
series = {ACL '92},
year = {1992},
location = {Newark, Delaware},
pages = {232--240},
numpages = {9},
url = {https://doi.org/10.3115/981967.981997},
doi = {10.3115/981967.981997},
acmid = {981997},
publisher = {Association for Computational Linguistics},
address = {Stroudsburg, PA, USA},
}
@inproceedings{kim2016high,
title={High-fidelity lexical axiom construction from verb glosses},
author={Kim, Gene and Schubert, Lenhart},
booktitle={Proceedings of the Fifth Joint Conference on Lexical and Computational Semantics},
pages={34--44},
year={2016}
}
@incollection{rumelhart1986,
author = {Rumelhart, D. E. and Hinton, G. E. and Williams, R. J.},
chapter = {Learning Internal Representations by Error Propagation},
title = {Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Vol. 1},
editor = {Rumelhart, David E. and McClelland, James L. and PDP Research Group, CORPORATE},
year = {1986},
isbn = {0-262-68053-X},
pages = {318--362},
numpages = {45},
url = {http://dl.acm.org/citation.cfm?id=104279.104293},
acmid = {104293},
publisher = {MIT Press},
address = {Cambridge, MA, USA},
}
@article{mcguinness2004owl,
title={OWL web ontology language overview},
author={McGuinness, Deborah L and Van Harmelen, Frank and others},
journal={W3C recommendation},
volume={10},
number={10},
pages={2004},
year={2004}
}
@article{morbini2009evaluation,
title={Evaluation of EPILOG: a reasoner for Episodic Logic},
author={Morbini, Fabrizio and Schubert, Lenhart K},
journal={Commonsense},
volume={9},
pages={1--3},
year={2009}
}
@inproceedings{mostafazadeh2017lsdsem,
title={Lsdsem 2017 shared task: The story cloze test},
author={Mostafazadeh, Nasrin and Roth, Michael and Louis, Annie and Chambers, Nathanael and Allen, James},
booktitle={Proceedings of the 2nd Workshop on Linking Models of Lexical, Sentential and Discourse-level Semantics},
pages={46--51},
year={2017}
}
@article{schubert2000episodic,
title={Episodic Logic meets Little Red Riding Hood: A comprehensive, natural representation for language understanding},
author={Schubert, Lenhart K and Hwang, Chung Hee},
journal={Natural language processing and knowledge representation: Language for Knowledge and Knowledge for Language},
pages={111--174},
year={2000},
publisher={MIT/AAAI Press Menlo Park, CA}
}
@article{firth57synopsis,
abstract = {Reprinted in:  Palmer, F. R. (ed.) (1968). Selected Papers of J. R. Firth 1952-59, pages 168-205. Longmans, London. },
added-at = {2008-05-14T00:52:58.000+0200},
address = {Oxford},
author = {Firth, J. R.},
biburl = {https://www.bibsonomy.org/bibtex/25e3d6c72cdd123a638f71886d78f3c1e/brightbyte},
booktitle = {Studies in Linguistic Analysis (special volume of the Philological Society)},
interhash = {b4f769667fdd195b4a75f61f6388a52e},
intrahash = {5e3d6c72cdd123a638f71886d78f3c1e},
keywords = {classic linguistics meanign relatedness semantic},
pages = {1-32},
publisher = {The Philological Society},
timestamp = {2009-01-23T09:58:50.000+0100},
title = {A synopsis of linguistic theory 1930-55.},
volume = {1952-59},
year = 1957
}
@article{berners2001semantic,
title={The semantic web},
author={Berners-Lee, Tim and Hendler, James and Lassila, Ora and others},
journal={Scientific american},
volume={284},
number={5},
pages={28--37},
year={2001},
publisher={New York, NY, USA:}
}
@incollection{brachman1989overview,
title={An overview of the KL-ONE knowledge representation system},
author={Brachman, Ronald J and Schmolze, James G},
booktitle={Readings in artificial intelligence and databases},
pages={207--230},
year={1989},
publisher={Elsevier}
}
@article{codd1970,
author = {Codd, E. F.},
title = {A Relational Model of Data for Large Shared Data Banks},
journal = {Commun. ACM},
issue_date = {June 1970},
volume = {13},
number = {6},
month = jun,
year = {1970},
issn = {0001-0782},
pages = {377--387},
numpages = {11},
url = {http://doi.acm.org/10.1145/362384.362685},
doi = {10.1145/362384.362685},
acmid = {362685},
publisher = {ACM},
address = {New York, NY, USA},
keywords = {composition, consistency, data bank, data base, data integrity, data organization, data structure, derivability, hierarchies of data, join, networks of data, predicate calculus, redundancy, relations, retrieval language, security},
}
@article{devlin2018bert,
title={Bert: Pre-training of deep bidirectional transformers for language understanding},
author={Devlin, Jacob and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina},
journal={arXiv preprint arXiv:1810.04805},
year={2018}
}
@Inbook{Bengio2006,
author="Bengio, Yoshua
and Schwenk, Holger
and Sen{\'e}cal, Jean-S{\'e}bastien
and Morin, Fr{\'e}deric
and Gauvain, Jean-Luc",
editor="Holmes, Dawn E.
and Jain, Lakhmi C.",
title="Neural Probabilistic Language Models",
bookTitle="Innovations in Machine Learning: Theory and Applications",
year="2006",
publisher="Springer Berlin Heidelberg",
address="Berlin, Heidelberg",
pages="137--186",
doi="10.1007/3-540-33486-6_6",
url="https://doi.org/10.1007/3-540-33486-6_6"
}
@inproceedings{Schubert:2002:WDG:1289189.1289263,
author = {Schubert, Lenhart},
title = {Can We Derive General World Knowledge from Texts?},
booktitle = {Proceedings of the Second International Conference on Human Language Technology Research},
series = {HLT '02},
year = {2002},
location = {San Diego, California},
pages = {94--97},
numpages = {4},
url = {http://dl.acm.org/citation.cfm?id=1289189.1289263},
acmid = {1289263},
publisher = {Morgan Kaufmann Publishers Inc.},
}
@inproceedings{schubert2015,
title={What kinds of knowledge are needed for genuine understanding},
author={Schubert, Lenhart K},
booktitle={IJCAI 2015 Workshop on Cognitive Knowledge Acquisition and Applications (Cognitum 2015)},
year={2015}
}
@article{opensesame,
author    = {Swabha Swayamdipta and
Sam Thomson and
Chris Dyer and
Noah A. Smith},
title     = {Frame-Semantic Parsing with Softmax-Margin Segmental RNNs and a Syntactic
Scaffold},
journal   = {CoRR},
volume    = {abs/1706.09528},
year      = {2017},
url       = {http://arxiv.org/abs/1706.09528},
archivePrefix = {arXiv},
eprint    = {1706.09528},
timestamp = {Mon, 13 Aug 2018 16:46:11 +0200},
biburl    = {https://dblp.org/rec/bib/journals/corr/SwayamdiptaTDS17},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
@article{lytinen1992conceptual,
title={Conceptual dependency and its descendants},
author={Lytinen, Steven L},
journal={Computers \& Mathematics with Applications},
volume={23},
number={2-5},
pages={51--73},
year={1992},
publisher={Elsevier}
}
@inproceedings{framenetspatial,
title = "Representing Spatial Relations in FrameNet",
author = "Petruck, Miriam R. L.  and
Ellsworth, Michael J.",
booktitle = "Proceedings of the First International Workshop on Spatial Language Understanding",
month = jun,
year = "2018",
address = "New Orleans",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/W18-1405",
doi = "10.18653/v1/W18-1405",
pages = "41--45",
abstract = "While humans use natural language to express spatial relations between and across entities in the world with great facility, natural language systems have a facility that depends on that human facility. This position paper presents approach to representing spatial relations in language, and advocates its adoption for representing the meaning of spatial language. This work shows the importance of axis-orientation systems for capturing the complexity of spatial relations, which FrameNet encodes with semantic types.",
}
@inproceedings{shen2007using,
title={Using semantic roles to improve question answering},
author={Shen, Dan and Lapata, Mirella},
booktitle={Proceedings of the 2007 joint conference on empirical methods in natural language processing and computational natural language learning (EMNLP-CoNLL)},
year={2007}
}
@inproceedings{yago,
title={Yago: a core of semantic knowledge},
author={Suchanek, Fabian M and Kasneci, Gjergji and Weikum, Gerhard},
booktitle={Proceedings of the 16th international conference on World Wide Web},
pages={697--706},
year={2007},
organization={ACM}
}
@inproceedings{rahman2011,
author = {Rahman, Altaf and Ng, Vincent},
title = {Coreference Resolution with World Knowledge},
booktitle = {Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1},
series = {HLT '11},
year = {2011},
isbn = {978-1-932432-87-9},
location = {Portland, Oregon},
pages = {814--824},
numpages = {11},
url = {http://dl.acm.org/citation.cfm?id=2002472.2002575},
acmid = {2002575},
publisher = {Association for Computational Linguistics},
}
@inproceedings{thompson2003generative,
title={A generative model for semantic role labeling},
author={Thompson, Cynthia A and Levy, Roger and Manning, Christopher D},
booktitle={European Conference on Machine Learning},
pages={397--408},
year={2003},
organization={Springer}
}
@inproceedings{giuglea2006semantic,
title={Semantic role labeling via framenet, verbnet and propbank},
author={Giuglea, Ana-Maria and Moschitti, Alessandro},
booktitle={Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics},
pages={929--936},
year={2006},
organization={Association for Computational Linguistics}
}
@article{semafor,
author = {Das, Dipanjan and Chen, Desai and Martins, André F. T. and Schneider, Nathan and Smith, Noah A.},
title = {Frame-Semantic Parsing},
journal = {Computational Linguistics},
volume = {40},
number = {1},
pages = {9-56},
year = {2014},
doi = {10.1162/COLI\_a\_00163}
}

@article{fillmore1976frame,
title={Frame semantics and the nature of language},
author={Fillmore, Charles J},
journal={Annals of the New York Academy of Sciences},
volume={280},
number={1},
pages={20--32},
year={1976},
publisher={Wiley Online Library}
}
@article{gildea2002automatic,
title={Automatic labeling of semantic roles},
author={Gildea, Daniel and Jurafsky, Daniel},
journal={Computational linguistics},
volume={28},
number={3},
pages={245--288},
year={2002},
publisher={MIT Press}
}
@book{Charniak:1980:AIP:576572,
editor = {Charniak, Eugene and Meehan, James R. and Reisbeck, Christopher K. and McDermott, Drew V.},
title = {Artificial Intelligence Programming},
year = {1980},
isbn = {0898596092},
publisher = {L. Erlbaum Associates Inc.},
address = {Hillsdale, NJ, USA},
}
@inproceedings{yamada2004automatic,
title={Automatic discovery of telic and agentive roles from corpus data},
author={Yamada, Ichiro and Baldwin, Timothy},
booktitle={Proceedings of the 18th Pacific Asia Conference on Language, Information and Computation},
pages={115--126},
year={2004}
}
@article{miller1995wordnet,
title={WordNet: a lexical database for English},
author={Miller, George A},
journal={Communications of the ACM},
volume={38},
number={11},
pages={39--41},
year={1995},
publisher={ACM}
}
@article{elmo,
title={Deep contextualized word representations},
author={Peters, Matthew E and Neumann, Mark and Iyyer, Mohit and Gardner, Matt and Clark, Christopher and Lee, Kenton and Zettlemoyer, Luke},
journal={arXiv preprint arXiv:1802.05365},
year={2018}
}
@article{pustejovsky1991generative,
title={The generative lexicon},
author={Pustejovsky, James},
journal={Computational linguistics},
volume={17},
number={4},
pages={409--441},
year={1991},
publisher={MIT press}
}
@article{allen2013evolution,
title={The evolution of episodic memory},
author={Allen, Timothy A and Fortin, Norbert J},
journal={Proceedings of the National Academy of Sciences},
volume={110},
number={Supplement 2},
pages={10379--10386},
year={2013},
publisher={National Acad Sciences}
}
@article{repisodic,
author = {Neisser, Ulric},
year = {1981},
month = {03},
pages = {1-22},
title = {John Dean's memory: A case study},
volume = {9},
journal = {Cognition},
doi = {10.1016/0010-0277(81)90011-1}
}
@inproceedings{framenet,
author = {Baker, Collin F. and Fillmore, Charles J. and Lowe, John B.},
title = {The Berkeley FrameNet Project},
booktitle = {Proceedings of the 36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics - Volume 1},
series = {ACL '98/COLING '98},
year = {1998},
location = {Montreal, Quebec, Canada},
pages = {86--90},
numpages = {5},
url = {https://doi.org/10.3115/980845.980860},
doi = {10.3115/980845.980860},
acmid = {980860},
publisher = {Association for Computational Linguistics},
}
@article{tulving1972episodic,
title={Episodic and semantic memory},
author={Tulving, Endel and others},
journal={Organization of memory},
volume={1},
pages={381--403},
year={1972},
publisher={New York}
}
@article{schank1980language,
title={Language and memory},
author={Schank, Roger C},
journal={Cognitive science},
volume={4},
number={3},
pages={243--284},
year={1980},
publisher={Elsevier}
}
@article{kolodner1983maintaining,
title={Maintaining organization in a dynamic long-term memory},
author={Kolodner, Janet L},
journal={Cognitive science},
volume={7},
number={4},
pages={243--280},
year={1983},
publisher={Elsevier}
}
@book{schank1982dynamic,
title={Dynamic memory: A theory of reminding and learning in computers and people},
author={Schank, Roger C},
volume={240},
year={1982},
publisher={Cambridge University Press Cambridge}
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author       =	{Schubert, Lenhart},
title        =	{Computational Linguistics},
booktitle    =	{The Stanford Encyclopedia of Philosophy},
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howpublished =	{\url{https://plato.stanford.edu/archives/spr2015/entries/computational-linguistics/}},
year         =	{2015},
edition      =	{Spring 2015},
publisher    =	{Metaphysics Research Lab, Stanford University}
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title = {A framework for representing knowledge},
year = {1975},
booktitle = {The psychology of computer vision},
publisher = {Cambridge, Massachusetts: McGraw-Hill},
}
@incollection{fillmore2010OHLA,
title={A frames approach to semantic analysis},
author={Fillmore, Charles J and Baker, Collin},
booktitle={The Oxford handbook of linguistic analysis},
editor={B. Heine and H. Narrog},
pages={313--340},
publisher={Oxford University Press},
year={2010},
}
@book{schank1977book,
author = {Schank, Roger C. and Abelson, Robert P.},
title = {Scripts, plans, goals and understanding: An inquiry into human knowledge structures},
year = {1977},
publisher = {Oxford, England: Lawrence Erlbaum},
}
@book{van-dijk1983book,
author = {van Dijk, Teun A. and Kintsch, Walter},
publisher = {New York: Academic Press},
title = {{Strategies of Discourse Comprehension}},
year = {1983},
}
@book{piaget1969book,
title={The Psychology of the Child},
author={Piaget, J. and Inhelder, B.},
year={1969},
publisher={New York: Basic Books}
}
@article{brewer1981CogPsych,
title = "Role of schemata in memory for places",
journal = "Cognitive Psychology",
volume = "13",
number = "2",
pages = "207--230",
year = "1981",
issn = "0010-0285",
doi = "https://doi.org/10.1016/0010-0285(81)90008-6",
author = "William F. Brewer and James C. Treyens",
}
@incollection{rumelhart1975chapter,
title = "NOTES ON A SCHEMA FOR STORIES",
editor = "DANIEL G. BOBROW and ALLAN COLLINS",
booktitle = "Representation and Understanding",
publisher = "Morgan Kaufmann",
address = "San Diego",
pages = "211--236",
year = "1975",
isbn = "978-0-12-108550-6",
doi = "https://doi.org/10.1016/B978-0-12-108550-6.50013-6",
url = "http://www.sciencedirect.com/science/article/pii/B9780121085506500136",
author = "David E. Rumelhart",
}
@article{bobrow1976CogSci,
title={On Overview of {KRL}, a Knowledge Representation Language},
author={Daniel G. Bobrow and Terry Winograd},
journal={Cognitive Science},
year={1976},
volume={1},
pages={3--46}
}
@article{fikes1985RFR,
author = {Fikes, Richard and Kehler, Tom},
title = {The Role of Frame-based Representation in Reasoning},
journal = {Commun. ACM},
issue_date = {Sept. 1985},
volume = {28},
number = {9},
month = sep,
year = {1985},
issn = {0001-0782},
pages = {904--920},
numpages = {17},
url = {http://doi.acm.org/10.1145/4284.4285},
doi = {10.1145/4284.4285},
acmid = {4285},
publisher = {ACM},
address = {New York, NY, USA},
}
@article{macgregor1991IEEE,
title={Using a description classifier to enhance knowledge representation},
author={Robert M. MacGregor and Mark H. Burstein},
journal={IEEE Expert},
year={1991},
volume={6},
pages={41--46}
}
@InProceedings{chambers2011ACL,
author = 	"Chambers, Nathanael and Jurafsky, Dan",
title = 	"Template-Based Information Extraction without the Templates",
booktitle = 	"Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies",
year = 	"2011",
publisher = 	"Association for Computational Linguistics",
pages = 	"976--986",
location = 	"Portland, Oregon, USA",
url = 	"http://aclweb.org/anthology/P11-1098"
}
@InProceedings{chambers2013EMNLP,
author = "Chambers, Nathanael",
title = "Event Schema Induction with a Probabilistic Entity-Driven Model",
booktitle = "Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing",
year = "2013",
publisher = "Association for Computational Linguistics",
pages = "1797--1807",
location = "Seattle, Washington, USA",
url = "http://aclweb.org/anthology/D13-1185"
}
@InProceedings{pichotta2016WS,
author = "Pichotta, Karl and Mooney, Raymond",
title = "Statistical Script Learning with Recurrent Neural Networks",
booktitle = "Proceedings of the Workshop on Uphill Battles in Language Processing: Scaling Early Achievements to Robust Methods",
year = "2016",
publisher = "Association for Computational Linguistics",
pages = "11--16",
location = "Austin, TX",
doi = "10.18653/v1/W16-6003",
url = "http://aclweb.org/anthology/W16-6003"
}
@inproceedings{yuan2018OEP,
author = {Yuan, Quan and Ren, Xiang and He, Wenqi and Zhang, Chao and Geng, Xinhe and Huang, Lifu and Ji, Heng and Lin, Chin-Yew and Han, Jiawei},
title = {Open-Schema Event Profiling for Massive News Corpora},
booktitle = {Proceedings of the 27th ACM International Conference on Information and Knowledge Management},
series = {CIKM '18},
year = {2018},
isbn = {978-1-4503-6014-2},
location = {Torino, Italy},
pages = {587--596},
numpages = {10},
url = {http://doi.acm.org/10.1145/3269206.3271674},
doi = {10.1145/3269206.3271674},
acmid = {3271674},
publisher = {ACM},
address = {New York, NY, USA},
keywords = {event detection, news summarization, schema induction},
}
@InProceedings{wanzare2017LSDSem,
author = "Wanzare, Lilian
and Zarcone, Alessandra
and Thater, Stefan
and Pinkal, Manfred",
title = "Inducing Script Structure from Crowdsourced Event Descriptions via Semi-Supervised Clustering",
booktitle = "Proceedings of the 2nd Workshop on Linking Models of Lexical, Sentential and Discourse-level Semantics",
year = "2017",
publisher = "Association for Computational Linguistics",
pages = "1--11",
location = "Valencia, Spain",
doi = "10.18653/v1/W17-0901",
url = "http://aclweb.org/anthology/W17-0901",
}
@techreport{schaeffer1993EPILOG,
author = {Schaeffer, S. and Hwang, C. and de Haan, J. and Schubert, L.},
title  = {{\sc Epilog}, the computational system for episodic logic: User's guide},
year = {1993},
institution = {Department of Computer Science, University of Alberta},
}
}
@inproceedings{gpt-too,
title = "{GPT}-too: A Language-Model-First Approach for {AMR}-to-Text Generation",
author = "Mager, Manuel  and
Fernandez Astudillo, Ram{\'o}n  and
Naseem, Tahira  and
Sultan, Md Arafat  and
Lee, Young-Suk  and
Florian, Radu  and
Roukos, Salim",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.167",
doi = "10.18653/v1/2020.acl-main.167",
pages = "1846--1852",
abstract = "Abstract Meaning Representations (AMRs) are broad-coverage sentence-level semantic graphs. Existing approaches to generating text from AMR have focused on training sequence-to-sequence or graph-to-sequence models on AMR annotated data only. In this paper, we propose an alternative approach that combines a strong pre-trained language model with cycle consistency-based re-scoring. Despite the simplicity of the approach, our experimental results show these models outperform all previous techniques on the English LDC2017T10 dataset, including the recent use of transformer architectures. In addition to the standard evaluation metrics, we provide human evaluation experiments that further substantiate the strength of our approach.",
}
@inproceedings{amr,
title = "{A}bstract {M}eaning {R}epresentation for Sembanking",
author = "Banarescu, Laura  and
Bonial, Claire  and
Cai, Shu  and
Georgescu, Madalina  and
Griffitt, Kira  and
Hermjakob, Ulf  and
Knight, Kevin  and
Koehn, Philipp  and
Palmer, Martha  and
Schneider, Nathan",
booktitle = "Proceedings of the 7th Linguistic Annotation Workshop and Interoperability with Discourse",
month = aug,
year = "2013",
address = "Sofia, Bulgaria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W13-2322",
pages = "178--186",
}
@inproceedings{kim-etal-2020-monotonic,
title={Monotonic Inference for Underspecified Episodic Logic},
author={Kim, Gene Louis and Juvekar, Mandar and Schubert, Lenhart},
booktitle={Proceedings of the Workshop Natural Logic Meets Machine Learning},
year={2020},
month={July},
dates={12--17},
location={Waltham, USA},
}
@article{Radford2019LanguageMA,
  title={Language models are unsupervised multitask learners},
  author={Radford, Alec and Wu, Jeffrey and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya and others},
  journal={OpenAI blog},
  volume={1},
  number={8},
  pages={9},
  year={2019}
}
@inproceedings{cai-knight-2013-smatch,
title = "{S}match: an Evaluation Metric for Semantic Feature Structures",
author = "Cai, Shu  and
Knight, Kevin",
booktitle = "Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = aug,
year = "2013",
address = "Sofia, Bulgaria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P13-2131",
pages = "748--752",
}
@inproceedings{papineni-etal-2002-bleu,
title = "{B}leu: a Method for Automatic Evaluation of Machine Translation",
author = "Papineni, Kishore  and
Roukos, Salim  and
Ward, Todd  and
Zhu, Wei-Jing",
booktitle = "Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2002",
address = "Philadelphia, Pennsylvania, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P02-1040",
doi = "10.3115/1073083.1073135",
pages = "311--318",
}
@inproceedings{ma-etal-2019-results,
title = "Results of the {WMT}19 Metrics Shared Task: Segment-Level and Strong {MT} Systems Pose Big Challenges",
author = "Ma, Qingsong  and
Wei, Johnny  and
Bojar, Ond{\v{r}}ej  and
Graham, Yvette",
booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
month = aug,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-5302",
doi = "10.18653/v1/W19-5302",
pages = "62--90",
abstract = "This paper presents the results of the WMT19 Metrics Shared Task. Participants were asked to score the outputs of the translations systems competing in the WMT19 News Translation Task with automatic metrics. 13 research groups submitted 24 metrics, 10 of which are reference-less {``}metrics{''} and constitute submissions to the joint task with WMT19 Quality Estimation Task, {``}QE as a Metric{''}. In addition, we computed 11 baseline metrics, with 8 commonly applied baselines (BLEU, SentBLEU, NIST, WER, PER, TER, CDER, and chrF) and 3 reimplementations (chrF+, sacreBLEU-BLEU, and sacreBLEU-chrF). Metrics were evaluated on the system level, how well a given metric correlates with the WMT19 official manual ranking, and segment level, how well the metric correlates with human judgements of segment quality. This year, we use direct assessment (DA) as our only form of manual evaluation.",
}
@inproceedings{banerjee-lavie-2005-meteor,
title = "{METEOR}: An Automatic Metric for {MT} Evaluation with Improved Correlation with Human Judgments",
author = "Banerjee, Satanjeev  and
Lavie, Alon",
booktitle = "Proceedings of the {ACL} Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization",
month = jun,
year = "2005",
address = "Ann Arbor, Michigan",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W05-0909",
pages = "65--72",
}
@inproceedings{popovic-2017-chrf,
title = "chr{F}++: words helping character n-grams",
author = "Popovi{\'c}, Maja",
booktitle = "Proceedings of the Second Conference on Machine Translation",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-4770",
doi = "10.18653/v1/W17-4770",
pages = "612--618",
}
@inproceedings{flanigan-etal-2016-generation,
title = "Generation from {A}bstract {M}eaning {R}epresentation using Tree Transducers",
author = "Flanigan, Jeffrey  and
Dyer, Chris  and
Smith, Noah A.  and
Carbonell, Jaime",
booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2016",
address = "San Diego, California",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N16-1087",
doi = "10.18653/v1/N16-1087",
pages = "731--739",
}
@inproceedings{pourdamghani-etal-2016-generating,
title = "Generating {E}nglish from {A}bstract {M}eaning {R}epresentations",
author = "Pourdamghani, Nima  and
Knight, Kevin  and
Hermjakob, Ulf",
booktitle = "Proceedings of the 9th International Natural Language Generation conference",
month = sep # " 5-8",
year = "2016",
address = "Edinburgh, UK",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W16-6603",
doi = "10.18653/v1/W16-6603",
pages = "21--25",
}
@article{guo-etal-2019-densely,
title = "Densely Connected Graph Convolutional Networks for Graph-to-Sequence Learning",
author = "Guo, Zhijiang  and
Zhang, Yan  and
Teng, Zhiyang  and
Lu, Wei",
journal = "Transactions of the Association for Computational Linguistics",
volume = "7",
month = mar,
year = "2019",
url = "https://aclanthology.org/Q19-1019",
doi = "10.1162/tacl_a_00269",
pages = "297--312",
abstract = "We focus on graph-to-sequence learning, which can be framed as transducing graph structures to sequences for text generation. To capture structural information associated with graphs, we investigate the problem of encoding graphs using graph convolutional networks (GCNs). Unlike various existing approaches where shallow architectures were used for capturing local structural information only, we introduce a dense connection strategy, proposing a novel Densely Connected Graph Convolutional Network (DCGCN). Such a deep architecture is able to integrate both local and non-local features to learn a better structural representation of a graph. Our model outperforms the state-of-the-art neural models significantly on AMR-to-text generation and syntax-based neural machine translation.",
}
@inproceedings{zhu-etal-2019-modeling,
title = "Modeling Graph Structure in Transformer for Better {AMR}-to-Text Generation",
author = "Zhu, Jie  and
Li, Junhui  and
Zhu, Muhua  and
Qian, Longhua  and
Zhang, Min  and
Zhou, Guodong",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1548",
doi = "10.18653/v1/D19-1548",
pages = "5459--5468",
abstract = "Recent studies on AMR-to-text generation often formalize the task as a sequence-to-sequence (seq2seq) learning problem by converting an Abstract Meaning Representation (AMR) graph into a word sequences. Graph structures are further modeled into the seq2seq framework in order to utilize the structural information in the AMR graphs. However, previous approaches only consider the relations between directly connected concepts while ignoring the rich structure in AMR graphs. In this paper we eliminate such a strong limitation and propose a novel structure-aware self-attention approach to better model the relations between indirectly connected concepts in the state-of-the-art seq2seq model, i.e. the Transformer. In particular, a few different methods are explored to learn structural representations between two concepts. Experimental results on English AMR benchmark datasets show that our approach significantly outperforms the state-of-the-art with 29.66 and 31.82 BLEU scores on LDC2015E86 and LDC2017T10, respectively. To the best of our knowledge, these are the best results achieved so far by supervised models on the benchmarks.",
}
@article{kim2021transition,
title={A transition-based parser for unscoped episodic logical forms},
author={Kim, Gene Louis and Duong, Viet and Lu, Xin and Schubert, Lenhart},
journal={arXiv preprint arXiv:2103.08759},
year={2021}
}
@inproceedings{cai2020graph,
title={Graph transformer for graph-to-sequence learning},
author={Cai, Deng and Lam, Wai},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
volume={34},
number={05},
pages={7464--7471},
year={2020}
}
@inproceedings{song-gildea-2019-sembleu,
title = "{S}em{B}leu: A Robust Metric for {AMR} Parsing Evaluation",
author = "Song, Linfeng  and
Gildea, Daniel",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-1446",
doi = "10.18653/v1/P19-1446",
pages = "4547--4552",
abstract = "Evaluating AMR parsing accuracy involves comparing pairs of AMR graphs. The major evaluation metric, SMATCH (Cai and Knight, 2013), searches for one-to-one mappings between the nodes of two AMRs with a greedy hill-climbing algorithm, which leads to search errors. We propose SEMBLEU, a robust metric that extends BLEU (Papineni et al., 2002) to AMRs. It does not suffer from search errors and considers non-local correspondences in addition to local ones. SEMBLEU is fully content-driven and punishes situations where a system{'}s output does not preserve most information from the input. Preliminary experiments on both sentence and corpus levels show that SEMBLEU has slightly higher consistency with human judgments than SMATCH. Our code is available at http://github.com/ freesunshine0316/sembleu.",
}
@inproceedings{beck-etal-2018-graph,
title = "Graph-to-Sequence Learning using Gated Graph Neural Networks",
author = "Beck, Daniel  and
Haffari, Gholamreza  and
Cohn, Trevor",
booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2018",
address = "Melbourne, Australia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P18-1026",
doi = "10.18653/v1/P18-1026",
pages = "273--283",
abstract = "Many NLP applications can be framed as a graph-to-sequence learning problem. Previous work proposing neural architectures on graph-to-sequence obtained promising results compared to grammar-based approaches but still rely on linearisation heuristics and/or standard recurrent networks to achieve the best performance. In this work propose a new model that encodes the full structural information contained in the graph. Our architecture couples the recently proposed Gated Graph Neural Networks with an input transformation that allows nodes and edges to have their own hidden representations, while tackling the parameter explosion problem present in previous work. Experimental results shows that our model outperforms strong baselines in generation from AMR graphs and syntax-based neural machine translation.",
}
@book{Aho:72,
author  = {Alfred V. Aho and Jeffrey D. Ullman},
title   = {The Theory of Parsing, Translation and Compiling},
year    = "1972",
volume  = "1",
publisher = {Prentice-Hall},
address = {Englewood Cliffs, NJ}
}
@book{APA:83,
author  = {{American Psychological Association}},
title   = {Publications Manual},
year    = "1983",
publisher = {American Psychological Association},
address = {Washington, DC}
}
@inproceedings{Gardner2017AllenNLP,
    title = "{A}llen{NLP}: A Deep Semantic Natural Language Processing Platform",
    author = "Gardner, Matt  and
      Grus, Joel  and
      Neumann, Mark  and
      Tafjord, Oyvind  and
      Dasigi, Pradeep  and
      Liu, Nelson F.  and
      Peters, Matthew  and
      Schmitz, Michael  and
      Zettlemoyer, Luke",
    booktitle = "Proceedings of Workshop for {NLP} Open Source Software ({NLP}-{OSS})",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W18-2501",
    doi = "10.18653/v1/W18-2501",
    pages = "1--6",
    abstract = "Modern natural language processing (NLP) research requires writing code. Ideally this code would provide a precise definition of the approach, easy repeatability of results, and a basis for extending the research. However, many research codebases bury high-level parameters under implementation details, are challenging to run and debug, and are difficult enough to extend that they are more likely to be rewritten. This paper describes AllenNLP, a library for applying deep learning methods to NLP research that addresses these issues with easy-to-use command-line tools, declarative configuration-driven experiments, and modular NLP abstractions. AllenNLP has already increased the rate of research experimentation and the sharing of NLP components at the Allen Institute for Artificial Intelligence, and we are working to have the same impact across the field.",
}
@inproceedings{10.3115/1219840.1219862,
author = {Charniak, Eugene and Johnson, Mark},
title = {Coarse-to-Fine n-Best Parsing and MaxEnt Discriminative Reranking},
year = {2005},
publisher = {Association for Computational Linguistics},
address = {USA},
url = {https://doi.org/10.3115/1219840.1219862},
doi = {10.3115/1219840.1219862},
booktitle = {Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics},
pages = {173–180},
numpages = {8},
location = {Ann Arbor, Michigan},
series = {ACL ’05}
}
@inproceedings{charniak2000maximum,
title={A maximum-entropy-inspired parser},
author={Charniak, Eugene},
booktitle={Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference},
pages={132--139},
year={2000},
organization={Association for Computational Linguistics}
}
@article{Chandra:81,
author = {Ashok K. Chandra and Dexter C. Kozen and Larry J. Stockmeyer},
year = "1981",
title = {Alternation},
journal = {Journal of the Association for Computing Machinery},
volume = "28",
number = "1",
pages = "114--133",
doi = "10.1145/322234.322243",
}
@inproceedings{andrew2007scalable,
title={Scalable training of {L1}-regularized log-linear models},
author={Andrew, Galen and Gao, Jianfeng},
booktitle={Proceedings of the 24th International Conference on Machine Learning},
pages={33--40},
year={2007},
}
@book{Gusfield:97,
author  = {Dan Gusfield},
title   = {Algorithms on Strings, Trees and Sequences},
year    = "1997",
publisher = {Cambridge University Press},
address = {Cambridge, UK}
}
@article{rasooli-tetrault-2015,
author    = {Mohammad Sadegh Rasooli and Joel R. Tetreault},
title     = {Yara Parser: {A} Fast and Accurate Dependency Parser},
journal   = {Computing Research Repository},
volume    = {arXiv:1503.06733},
year      = {2015},
url       = {http://arxiv.org/abs/1503.06733},
note    = {version 2}
}
@article{Ando2005,
Acmid = {1194905},
Author = {Ando, Rie Kubota and Zhang, Tong},
Issn = {1532-4435},
Issue_Date = {12/1/2005},
Journal = {Journal of Machine Learning Research},
Month = dec,
Numpages = {37},
Pages = {1817--1853},
Publisher = {JMLR.org},
Title = {A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data},
Volume = {6},
Year = {2005}}
@book{mooney1990general,
title={A general explanation-based learning mechanism and its application to narrative understanding},
author={Mooney, Raymond J},
year={1990},
publisher={Morgan Kaufmann}
}
@article{mostafazadeh2016corpus,
title={A corpus and evaluation framework for deeper understanding of commonsense stories},
author={Mostafazadeh, Nasrin and Chambers, Nathanael and He, Xiaodong and Parikh, Devi and Batra, Dhruv and Vanderwende, Lucy and Kohli, Pushmeet and Allen, James},
journal={arXiv preprint arXiv:1604.01696},
year={2016}
}
@inproceedings{ganitkevitch2013ppdb,
title={PPDB: The paraphrase database},
author={Ganitkevitch, Juri and Van Durme, Benjamin and Callison-Burch, Chris},
booktitle={Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies},
pages={758--764},
year={2013}
}
@inproceedings{kim2019generating,
title={Generating Discourse Inferences from Unscoped Episodic Logical Formulas},
author={Kim, Gene and Kane, Benjamin and Duong, Viet and Mendiratta, Muskaan and McGuire, Graeme and Sackstein, Sophie and Platonov, Georgiy and Schubert, Lenhart},
booktitle={Proceedings of the First International Workshop on Designing Meaning Representations},
pages={56--65},
year={2019}
}
@inproceedings{10.1145/2187836.2187958,
author = {Radinsky, Kira and Davidovich, Sagie and Markovitch, Shaul},
title = {Learning Causality for News Events Prediction},
year = {2012},
isbn = {9781450312295},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2187836.2187958},
doi = {10.1145/2187836.2187958},
booktitle = {Proceedings of the 21st International Conference on World Wide Web},
pages = {909–918},
numpages = {10},
keywords = {news prediction, future prediction, web knowledge for future prediction},
location = {Lyon, France},
series = {WWW ’12}
}
@article{allen1983maintaining,
title={Maintaining knowledge about temporal intervals},
author={Allen, James F},
journal={Communications of the ACM},
volume={26},
number={11},
pages={832--843},
year={1983},
publisher={ACM New York, NY, USA}
}
@inproceedings{gerevini1993efficient,
title={Efficient temporal reasoning through timegraphs},
author={Gerevini, Alfonso and Schubert, Lenhart},
booktitle={IJCAI},
pages={648--654},
year={1993}
}
@article{elnlog,
author = {Stratos, Karl and Schubert, Lenhart and Gordon, Jonathan},
year = {2011},
month = {01},
pages = {},
title = {Episodic Logic: Natural Logic + reasoning},
journal = {KEOD 2011 - Proceedings of the International Conference on Knowledge Engineering and Ontology Development}
}
@book{mcguffey,
author = {William Holmes McGuffey},
title = {The New McGuffey First Reader},
publisher = {American Book Company},
year = 1901,
}
@inproceedings{mostafazadeh-etal-2016-corpus,
title = "A Corpus and Cloze Evaluation for Deeper Understanding of Commonsense Stories",
author = "Mostafazadeh, Nasrin  and
Chambers, Nathanael  and
He, Xiaodong  and
Parikh, Devi  and
Batra, Dhruv  and
Vanderwende, Lucy  and
Kohli, Pushmeet  and
Allen, James",
booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2016",
address = "San Diego, California",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/N16-1098",
doi = "10.18653/v1/N16-1098",
pages = "839--849",
}
@inproceedings{damonte-cohen-2019-structural,
title = "Structural Neural Encoders for {AMR}-to-text Generation",
author = "Damonte, Marco  and
Cohen, Shay B.",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N19-1366",
doi = "10.18653/v1/N19-1366",
pages = "3649--3658",
abstract = "AMR-to-text generation is a problem recently introduced to the NLP community, in which the goal is to generate sentences from Abstract Meaning Representation (AMR) graphs. Sequence-to-sequence models can be used to this end by converting the AMR graphs to strings. Approaching the problem while working directly with graphs requires the use of graph-to-sequence models that encode the AMR graph into a vector representation. Such encoding has been shown to be beneficial in the past, and unlike sequential encoding, it allows us to explicitly capture reentrant structures in the AMR graphs. We investigate the extent to which reentrancies (nodes with multiple parents) have an impact on AMR-to-text generation by comparing graph encoders to tree encoders, where reentrancies are not preserved. We show that improvements in the treatment of reentrancies and long-range dependencies contribute to higher overall scores for graph encoders. Our best model achieves 24.40 BLEU on LDC2015E86, outperforming the state of the art by 1.1 points and 24.54 BLEU on LDC2017T10, outperforming the state of the art by 1.24 points.",
}
@article{guo-etal-2019-densely,
title = "Densely Connected Graph Convolutional Networks for Graph-to-Sequence Learning",
author = "Guo, Zhijiang  and
Zhang, Yan  and
Teng, Zhiyang  and
Lu, Wei",
journal = "Transactions of the Association for Computational Linguistics",
volume = "7",
month = mar,
year = "2019",
url = "https://aclanthology.org/Q19-1019",
doi = "10.1162/tacl_a_00269",
pages = "297--312",
abstract = "We focus on graph-to-sequence learning, which can be framed as transducing graph structures to sequences for text generation. To capture structural information associated with graphs, we investigate the problem of encoding graphs using graph convolutional networks (GCNs). Unlike various existing approaches where shallow architectures were used for capturing local structural information only, we introduce a dense connection strategy, proposing a novel Densely Connected Graph Convolutional Network (DCGCN). Such a deep architecture is able to integrate both local and non-local features to learn a better structural representation of a graph. Our model outperforms the state-of-the-art neural models significantly on AMR-to-text generation and syntax-based neural machine translation.",
}
@inproceedings{cao-clark-2019-factorising,
title = "Factorising {AMR} generation through syntax",
author = "Cao, Kris  and
Clark, Stephen",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N19-1223",
doi = "10.18653/v1/N19-1223",
pages = "2157--2163",
abstract = "Generating from Abstract Meaning Representation (AMR) is an underspecified problem, as many syntactic decisions are not specified by the semantic graph. To explicitly account for this variation, we break down generating from AMR into two steps: first generate a syntactic structure, and then generate the surface form. We show that decomposing the generation process this way leads to state-of-the-art single model performance generating from AMR without additional unlabelled data. We also demonstrate that we can generate meaning-preserving syntactic paraphrases of the same AMR graph, as judged by humans.",
}
@inproceedings{Sinh_Minh_2019, place={Cham}, title={A Study on Self-attention Mechanism for AMRto-text Generation}, ISBN={978-3-030-23281-8}, abstractNote={Introduced by Vaswani et al., transformer
architecture, with the effective use of self-attention mechanism, has shown outstanding performance in
translating sequence of text from one language to another. In this paper, we conduct experiments using the
self-attention in converting an abstract meaning representation (AMR) graph, a semantic representation, into
a natural language sentence, also known as the task of AMR-to-text generation. On the benchmark dataset for
this task, we obtain promising results comparing to existing deep learning methods in the literature.},
booktitle={Natural Language Processing and Information Systems}, publisher={Springer International
Publishing}, author={Sinh, Vu Trong and Minh, Nguyen Le}, editor={Métais, Elisabeth and Meziane, Farid
and Vadera, Sunil and Sugumaran, Vijayan and Saraee, Mohamad}, year={2019}, pages={321–328} }
@inproceedings{ribeiro-etal-2019-enhancing,
title = "Enhancing {AMR}-to-Text Generation with Dual Graph Representations",
author = "Ribeiro, Leonardo F. R.  and
Gardent, Claire  and
Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1314",
doi = "10.18653/v1/D19-1314",
pages = "3183--3194",
abstract = "Generating text from graph-based data, such as Abstract Meaning Representation (AMR), is a challenging task due to the inherent difficulty in how to properly encode the structure of a graph with labeled edges. To address this difficulty, we propose a novel graph-to-sequence model that encodes different but complementary perspectives of the structural information contained in the AMR graph. The model learns parallel top-down and bottom-up representations of nodes capturing contrasting views of the graph. We also investigate the use of different node message passing strategies, employing different state-of-the-art graph encoders to compute node representations based on incoming and outgoing perspectives. In our experiments, we demonstrate that the dual graph representation leads to improvements in AMR-to-text generation, achieving state-of-the-art results on two AMR datasets",
}
@article{Cai_Lam_2020, title={Graph Transformer for Graph-to-Sequence Learning}, volume={34},
DOI={10.1609/aaai.v34i05.6243}, number={05}, journal={Proceedings of the AAAI Conference on
Artificial Intelligence}, author={Cai, Deng and Lam, Wai}, year={2020}, month={Apr}, pages=
{7464–7471} }
@inproceedings{zhu-etal-2019-modeling,
title = "Modeling Graph Structure in Transformer for Better {AMR}-to-Text Generation",
author = "Zhu, Jie  and
Li, Junhui  and
Zhu, Muhua  and
Qian, Longhua  and
Zhang, Min  and
Zhou, Guodong",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1548",
doi = "10.18653/v1/D19-1548",
pages = "5459--5468",
abstract = "Recent studies on AMR-to-text generation often formalize the task as a sequence-to-sequence (seq2seq) learning problem by converting an Abstract Meaning Representation (AMR) graph into a word sequences. Graph structures are further modeled into the seq2seq framework in order to utilize the structural information in the AMR graphs. However, previous approaches only consider the relations between directly connected concepts while ignoring the rich structure in AMR graphs. In this paper we eliminate such a strong limitation and propose a novel structure-aware self-attention approach to better model the relations between indirectly connected concepts in the state-of-the-art seq2seq model, i.e. the Transformer. In particular, a few different methods are explored to learn structural representations between two concepts. Experimental results on English AMR benchmark datasets show that our approach significantly outperforms the state-of-the-art with 29.66 and 31.82 BLEU scores on LDC2015E86 and LDC2017T10, respectively. To the best of our knowledge, these are the best results achieved so far by supervised models on the benchmarks.",
}
@inproceedings{zhang-etal-2019-amr,
title = "{AMR} Parsing as Sequence-to-Graph Transduction",
author = "Zhang, Sheng  and
Ma, Xutai  and
Duh, Kevin  and
Van Durme, Benjamin",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-1009",
doi = "10.18653/v1/P19-1009",
pages = "80--94",
abstract = "We propose an attention-based model that treats AMR parsing as sequence-to-graph transduction. Unlike most AMR parsers that rely on pre-trained aligners, external semantic resources, or data augmentation, our proposed parser is aligner-free, and it can be effectively trained with limited amounts of labeled AMR data. Our experimental results outperform all previously reported SMATCH scores, on both AMR 2.0 (76.3{\%} on LDC2017T10) and AMR 1.0 (70.2{\%} on LDC2014T12).",
}
@inproceedings{cai-lam-2020-amr,
title = "{AMR} Parsing via Graph-Sequence Iterative Inference",
author = "Cai, Deng  and
Lam, Wai",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.119",
doi = "10.18653/v1/2020.acl-main.119",
pages = "1290--1301",
abstract = "We propose a new end-to-end model that treats AMR parsing as a series of dual decisions on the input sequence and the incrementally constructed graph. At each time step, our model performs multiple rounds of attention, reasoning, and composition that aim to answer two critical questions: (1) which part of the input \textit{sequence} to abstract; and (2) where in the output \textit{graph} to construct the new concept. We show that the answers to these two questions are mutually causalities. We design a model based on iterative inference that helps achieve better answers in both perspectives, leading to greatly improved parsing accuracy. Our experimental results significantly outperform all previously reported Smatch scores by large margins. Remarkably, without the help of any large-scale pre-trained language model (e.g., BERT), our model already surpasses previous state-of-the-art using BERT. With the help of BERT, we can push the state-of-the-art results to 80.2{\%} on LDC2017T10 (AMR 2.0) and 75.4{\%} on LDC2014T12 (AMR 1.0).",
}
@inproceedings{kasper-1989-flexible,
title = "A Flexible Interface for Linking Applications to {P}enman{'}s Sentence Generator",
author = "Kasper, Robert T.",
booktitle = "Speech and Natural Language: Proceedings of a Workshop Held at Philadelphia, {P}ennsylvania, {F}ebruary 21-23, 1989",
year = "1989",
url = "https://aclanthology.org/H89-1022",
}
@inproceedings{NEURIPS2020_1457c0d6,
author = {Brown, Tom and Mann, Benjamin and Ryder, Nick and Subbiah, Melanie and Kaplan, Jared D and Dhariwal, Prafulla and Neelakantan, Arvind and Shyam, Pranav and Sastry, Girish and Askell, Amanda and Agarwal, Sandhini and Herbert-Voss, Ariel and Krueger, Gretchen and Henighan, Tom and Child, Rewon and Ramesh, Aditya and Ziegler, Daniel and Wu, Jeffrey and Winter, Clemens and Hesse, Chris and Chen, Mark and Sigler, Eric and Litwin, Mateusz and Gray, Scott and Chess, Benjamin and Clark, Jack and Berner, Christopher and McCandlish, Sam and Radford, Alec and Sutskever, Ilya and Amodei, Dario},
booktitle = {Advances in Neural Information Processing Systems},
editor = {H. Larochelle and M. Ranzato and R. Hadsell and M. F. Balcan and H. Lin},
pages = {1877--1901},
publisher = {Curran Associates, Inc.},
title = {Language Models are Few-Shot Learners},
url = {https://proceedings.neurips.cc/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf},
volume = {33},
year = {2020}
}
@inproceedings{path_lm_schemas,
title = "Connecting the Dots: Event Graph Schema Induction with Path Language Modeling",
author = "Li, Manling  and
Zeng, Qi  and
Lin, Ying  and
Cho, Kyunghyun  and
Ji, Heng  and
May, Jonathan  and
Chambers, Nathanael  and
Voss, Clare",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-main.50",
doi = "10.18653/v1/2020.emnlp-main.50",
pages = "684--695",
abstract = "Event schemas can guide our understanding and ability to make predictions with respect to what might happen next. We propose a new Event Graph Schema, where two event types are connected through multiple paths involving entities that fill important roles in a coherent story. We then introduce Path Language Model, an auto-regressive language model trained on event-event paths, and select salient and coherent paths to probabilistically construct these graph schemas. We design two evaluation metrics, instance coverage and instance coherence, to evaluate the quality of graph schema induction, by checking when coherent event instances are covered by the schema graph. Intrinsic evaluations show that our approach is highly effective at inducing salient and coherent schemas. Extrinsic evaluations show the induced schema repository provides significant improvement to downstream end-to-end Information Extraction over a state-of-the-art joint neural extraction model, when used as additional global features to unfold instance graphs.",
}
@inproceedings{weber_causal_scripts,
title = "Causal Inference of Script Knowledge",
author = "Weber, Noah  and
Rudinger, Rachel  and
Van Durme, Benjamin",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-main.612",
doi = "10.18653/v1/2020.emnlp-main.612",
pages = "7583--7596",
abstract = "When does a sequence of events define an everyday scenario and how can this knowledge be induced from text? Prior works in inducing such scripts have relied on, in one form or another, measures of correlation between instances of events in a corpus. We argue from both a conceptual and practical sense that a purely correlation-based approach is insufficient, and instead propose an approach to script induction based on the causal effect between events, formally defined via interventions. Through both human and automatic evaluations, we show that the output of our method based on causal effects better matches the intuition of what a script represents.",
}
@inproceedings{norvig1987inference,
title={Inference in text understanding.},
author={Norvig, Peter},
booktitle={AAAI},
pages={561--565},
year={1987}
}
@inproceedings{lome,
title = "{LOME}: Large Ontology Multilingual Extraction",
author = "Xia, Patrick  and
Qin, Guanghui  and
Vashishtha, Siddharth  and
Chen, Yunmo  and
Chen, Tongfei  and
May, Chandler  and
Harman, Craig  and
Rawlins, Kyle  and
White, Aaron Steven  and
Van Durme, Benjamin",
booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations",
month = apr,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.eacl-demos.19",
doi = "10.18653/v1/2021.eacl-demos.19",
pages = "149--159",
abstract = "We present LOME, a system for performing multilingual information extraction. Given a text document as input, our core system identifies spans of textual entity and event mentions with a FrameNet (Baker et al., 1998) parser. It subsequently performs coreference resolution, fine-grained entity typing, and temporal relation prediction between events. By doing so, the system constructs an event and entity focused knowledge graph. We can further apply third-party modules for other types of annotation, like relation extraction. Our (multilingual) first-party modules either outperform or are competitive with the (monolingual) state-of-the-art. We achieve this through the use of multilingual encoders like XLM-R (Conneau et al., 2020) and leveraging multilingual training data. LOME is available as a Docker container on Docker Hub. In addition, a lightweight version of the system is accessible as a web demo.",
}
@unpublished{spacy2,
AUTHOR = {Honnibal, Matthew and Montani, Ines},
TITLE  = {{spaCy 2}: Natural language understanding with {B}loom embeddings, convolutional neural networks and incremental parsing},
YEAR   = {2017},
Note   = {To appear}
}
@inproceedings{Lawley2021LearningGE,
    title = "Learning General Event Schemas with Episodic Logic",
    author = "Lawley, Lane  and
      Kuehnert, Benjamin  and
      Schubert, Lenhart",
    booktitle = "Proceedings of the 1st and 2nd Workshops on Natural Logic Meets Machine Learning (NALOMA)",
    month = jun,
    year = "2021",
    address = "Groningen, the Netherlands (online)",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.naloma-1.1",
    pages = "1--6",
}
@inproceedings{lawleykimschubert2019,
    title = "Towards Natural Language Story Understanding with Rich Logical Schemas",
    author = "Lawley, Lane  and
      Kim, Gene Louis  and
      Schubert, Lenhart",
    booktitle = "Proceedings of the Sixth Workshop on Natural Language and Computer Science",
    month = may,
    year = "2019",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W19-1102",
    doi = "10.18653/v1/W19-1102",
    pages = "11--22",
}
@misc{gpt-j,
author = {Wang, Ben and Komatsuzaki, Aran},
title = {{GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model}},
howpublished = {\url{https://github.com/kingoflolz/mesh-transformer-jax}},
year = 2021,
month = May
}
@inproceedings{wolf-etal-2020-transformers,
title = "Transformers: State-of-the-Art Natural Language Processing",
author = "Thomas Wolf and Lysandre Debut and Victor Sanh and Julien Chaumond and Clement Delangue and Anthony Moi and Pierric Cistac and Tim Rault and Rémi Louf and Morgan Funtowicz and Joe Davison and Sam Shleifer and Patrick von Platen and Clara Ma and Yacine Jernite and Julien Plu and Canwen Xu and Teven Le Scao and Sylvain Gugger and Mariama Drame and Quentin Lhoest and Alexander M. Rush",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = oct,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.emnlp-demos.6",
pages = "38--45"
}
@inproceedings{kim2021naloma,
    title = "A (Mostly) Symbolic System for Monotonic Inference with Unscoped Episodic Logical Forms",
    author = "Kim, Gene  and
      Juvekar, Mandar  and
      Ekmekciu, Junis  and
      Duong, Viet  and
      Schubert, Lenhart",
    booktitle = "Proceedings of the 1st and 2nd Workshops on Natural Logic Meets Machine Learning (NALOMA)",
    month = jun,
    year = "2021",
    address = "Groningen, the Netherlands (online)",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.naloma-1.9",
    pages = "71--80",
    abstract = "We implement the formalization of natural logic-like monotonic inference using Unscoped Episodic Logical Forms (ULFs) by Kim et al. (2020). We demonstrate this system{'}s capacity to handle a variety of challenging semantic phenomena using the FraCaS dataset (Cooper et al., 1996). These results give empirical evidence for prior claims that ULF is an appropriate representation to mediate natural logic-like inferences.",
}
@phdthesis{kim2022corpus,
  title={Corpus annotation, parsing, and inference for episodic logic type structure},
  author={Kim, Gene Louis},
  year={2022},
  school={University of Rochester}
}
@inproceedings{gibson2022language,
  title={Language-Model-Based Parsing and English Generation for Unscoped Episodic Logical Forms},
  author={Gibson, Erin and Lawley, Lane},
  booktitle={The International FLAIRS Conference Proceedings},
  volume={35},
  month=may,
  year={2022},
}
@inproceedings{schubert-2014-treebank,
    title = "From Treebank Parses to Episodic Logic and Commonsense Inference",
    author = "Schubert, Lenhart",
    booktitle = "Proceedings of the {ACL} 2014 Workshop on Semantic Parsing",
    month = jun,
    year = "2014",
    address = "Baltimore, MD",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W14-2411",
    doi = "10.3115/v1/W14-2411",
    pages = "55--60",
}
@article{zipf-nl,
  title={Zipf’s word frequency law in natural language: A critical review and future directions},
  author={Steven T. Piantadosi},
  journal={Psychonomic Bulletin \& Review},
  year={2014},
  volume={21},
  pages={1112-1130}
}
@inproceedings{10.1145/511793.511799,
author = {Lebowitz, Michael},
title = {Intelligent Information Systems},
year = {1983},
isbn = {0897911075},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/511793.511799},
doi = {10.1145/511793.511799},
abstract = {Natural language processing techniques developed for Artificial Intelligence programs can aid in constructing powerful information retrieval systems in at least two areas. Automatic construction of new concepts allows a large body of information to be organized compactly and in a manner that allows a wide range of queries to be answered. Also, using natural language processing techniques to conceptually analyze the documents being stored in a system greatly expands the effectiveness of queries about given pieces of text. However, only robust conceptual analysis methods are adequate for such systems. This paper will discuss approaches to both concept learning, in the form of Generalization-Based Memory, and powerful, robust text processing achieved by Memory-Based Understanding. These techniques have been implemented in the computer systems IPP, a program that reads, remembers and generalizes from news stories about terrorism, and RESEARCHER, currently in the prototype stage, that operates in a very different domain (technical texts, patent abstracts in particular).},
booktitle = {Proceedings of the 6th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {25–30},
numpages = {6},
location = {Bethesda, Maryland},
series = {SIGIR '83}
}
@inproceedings{lebowitz-dnet,
author = {Lebowitz, Michael},
title = {The Nature of Generalization in Understanding},
year = {1981},
publisher = {Morgan Kaufmann Publishers Inc.},
address = {San Francisco, CA, USA},
abstract = {True understanding of natural language text requires the inclusion of generalization and long-term memory. This paper describes the generalization • process and! memory used in the Integrated Partial Parser (IPP), a computer program that reads and remembers news stories. The need for generalization and generalization-based memory as an integral part of understanding natural language text is illustrated with examples from IPP. In addition, the nature of generalization is discussed.},
booktitle = {Proceedings of the 7th International Joint Conference on Artificial Intelligence - Volume 1},
pages = {348–353},
numpages = {6},
location = {Vancouver, BC, Canada},
series = {IJCAI'81}
}
@inproceedings{lebowitz-dnet2,
author = {Lebowitz, Michael},
title = {Intelligent Information Systems},
year = {1983},
isbn = {0897911075},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/511793.511799},
doi = {10.1145/511793.511799},
abstract = {Natural language processing techniques developed for Artificial Intelligence programs can aid in constructing powerful information retrieval systems in at least two areas. Automatic construction of new concepts allows a large body of information to be organized compactly and in a manner that allows a wide range of queries to be answered. Also, using natural language processing techniques to conceptually analyze the documents being stored in a system greatly expands the effectiveness of queries about given pieces of text. However, only robust conceptual analysis methods are adequate for such systems. This paper will discuss approaches to both concept learning, in the form of Generalization-Based Memory, and powerful, robust text processing achieved by Memory-Based Understanding. These techniques have been implemented in the computer systems IPP, a program that reads, remembers and generalizes from news stories about terrorism, and RESEARCHER, currently in the prototype stage, that operates in a very different domain (technical texts, patent abstracts in particular).},
booktitle = {Proceedings of the 6th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {25–30},
numpages = {6},
location = {Bethesda, Maryland},
series = {SIGIR '83}
}
@misc{bigbench,
  doi = {10.48550/ARXIV.2206.04615},
  url = {https://arxiv.org/abs/2206.04615},
  author = {Srivastava, Aarohi and Rastogi, Abhinav and Rao, Abhishek and Shoeb, Abu Awal Md and Abid, Abubakar and Fisch, Adam and Brown, Adam R. and Santoro, Adam and Gupta, Aditya and Garriga-Alonso, Adrià and Kluska, Agnieszka and Lewkowycz, Aitor and Agarwal, Akshat and Power, Alethea and Ray, Alex and Warstadt, Alex and Kocurek, Alexander W. and Safaya, Ali and Tazarv, Ali and Xiang, Alice and Parrish, Alicia and Nie, Allen and Hussain, Aman and Askell, Amanda and Dsouza, Amanda and Slone, Ambrose and Rahane, Ameet and Iyer, Anantharaman S. and Andreassen, Anders and Madotto, Andrea and Santilli, Andrea and Stuhlmüller, Andreas and Dai, Andrew and La, Andrew and Lampinen, Andrew and Zou, Andy and Jiang, Angela and Chen, Angelica and Vuong, Anh and Gupta, Animesh and Gottardi, Anna and Norelli, Antonio and Venkatesh, Anu and Gholamidavoodi, Arash and Tabassum, Arfa and Menezes, Arul and Kirubarajan, Arun and Mullokandov, Asher and Sabharwal, Ashish and Herrick, Austin and Efrat, Avia and Erdem, Aykut and Karakaş, Ayla and Roberts, B. Ryan and Loe, Bao Sheng and Zoph, Barret and Bojanowski, Bartłomiej and Özyurt, Batuhan and Hedayatnia, Behnam and Neyshabur, Behnam and Inden, Benjamin and Stein, Benno and Ekmekci, Berk and Lin, Bill Yuchen and Howald, Blake and Diao, Cameron and Dour, Cameron and Stinson, Catherine and Argueta, Cedrick and Ramírez, César Ferri and Singh, Chandan and Rathkopf, Charles and Meng, Chenlin and Baral, Chitta and Wu, Chiyu and Callison-Burch, Chris and Waites, Chris and Voigt, Christian and Manning, Christopher D. and Potts, Christopher and Ramirez, Cindy and Rivera, Clara E. and Siro, Clemencia and Raffel, Colin and Ashcraft, Courtney and Garbacea, Cristina and Sileo, Damien and Garrette, Dan and Hendrycks, Dan and Kilman, Dan and Roth, Dan and Freeman, Daniel and Khashabi, Daniel and Levy, Daniel and González, Daniel Moseguí and Perszyk, Danielle and Hernandez, Danny and Chen, Danqi and Ippolito, Daphne and Gilboa, Dar and Dohan, David and Drakard, David and Jurgens, David and Datta, Debajyoti and Ganguli, Deep and Emelin, Denis and Kleyko, Denis and Yuret, Deniz and Chen, Derek and Tam, Derek and Hupkes, Dieuwke and Misra, Diganta and Buzan, Dilyar and Mollo, Dimitri Coelho and Yang, Diyi and Lee, Dong-Ho and Shutova, Ekaterina and Cubuk, Ekin Dogus and Segal, Elad and Hagerman, Eleanor and Barnes, Elizabeth and Donoway, Elizabeth and Pavlick, Ellie and Rodola, Emanuele and Lam, Emma and Chu, Eric and Tang, Eric and Erdem, Erkut and Chang, Ernie and Chi, Ethan A. and Dyer, Ethan and Jerzak, Ethan and Kim, Ethan and Manyasi, Eunice Engefu and Zheltonozhskii, Evgenii and Xia, Fanyue and Siar, Fatemeh and Martínez-Plumed, Fernando and Happé, Francesca and Chollet, Francois and Rong, Frieda and Mishra, Gaurav and Winata, Genta Indra and de Melo, Gerard and Kruszewski, Germán and Parascandolo, Giambattista and Mariani, Giorgio and Wang, Gloria and Jaimovitch-López, Gonzalo and Betz, Gregor and Gur-Ari, Guy and Galijasevic, Hana and Kim, Hannah and Rashkin, Hannah and Hajishirzi, Hannaneh and Mehta, Harsh and Bogar, Hayden and Shevlin, Henry and Schütze, Hinrich and Yakura, Hiromu and Zhang, Hongming and Wong, Hugh Mee and Ng, Ian and Noble, Isaac and Jumelet, Jaap and Geissinger, Jack and Kernion, Jackson and Hilton, Jacob and Lee, Jaehoon and Fisac, Jaime Fernández and Simon, James B. and Koppel, James and Zheng, James and Zou, James and Kocoń, Jan and Thompson, Jana and Kaplan, Jared and Radom, Jarema and Sohl-Dickstein, Jascha and Phang, Jason and Wei, Jason and Yosinski, Jason and Novikova, Jekaterina and Bosscher, Jelle and Marsh, Jennifer and Kim, Jeremy and Taal, Jeroen and Engel, Jesse and Alabi, Jesujoba and Xu, Jiacheng and Song, Jiaming and Tang, Jillian and Waweru, Joan and Burden, John and Miller, John and Balis, John U. and Berant, Jonathan and Frohberg, Jörg and Rozen, Jos and Hernandez-Orallo, Jose and Boudeman, Joseph and Jones, Joseph and Tenenbaum, Joshua B. and Rule, Joshua S. and Chua, Joyce and Kanclerz, Kamil and Livescu, Karen and Krauth, Karl and Gopalakrishnan, Karthik and Ignatyeva, Katerina and Markert, Katja and Dhole, Kaustubh D. and Gimpel, Kevin and Omondi, Kevin and Mathewson, Kory and Chiafullo, Kristen and Shkaruta, Ksenia and Shridhar, Kumar and McDonell, Kyle and Richardson, Kyle and Reynolds, Laria and Gao, Leo and Zhang, Li and Dugan, Liam and Qin, Lianhui and Contreras-Ochando, Lidia and Morency, Louis-Philippe and Moschella, Luca and Lam, Lucas and Noble, Lucy and Schmidt, Ludwig and He, Luheng and Colón, Luis Oliveros and Metz, Luke and Şenel, Lütfi Kerem and Bosma, Maarten and Sap, Maarten and ter Hoeve, Maartje and Farooqi, Maheen and Faruqui, Manaal and Mazeika, Mantas and Baturan, Marco and Marelli, Marco and Maru, Marco and Quintana, Maria Jose Ramírez and Tolkiehn, Marie and Giulianelli, Mario and Lewis, Martha and Potthast, Martin and Leavitt, Matthew L. and Hagen, Matthias and Schubert, Mátyás and Baitemirova, Medina Orduna and Arnaud, Melody and McElrath, Melvin and Yee, Michael A. and Cohen, Michael and Gu, Michael and Ivanitskiy, Michael and Starritt, Michael and Strube, Michael and Swędrowski, Michał and Bevilacqua, Michele and Yasunaga, Michihiro and Kale, Mihir and Cain, Mike and Xu, Mimee and Suzgun, Mirac and Tiwari, Mo and Bansal, Mohit and Aminnaseri, Moin and Geva, Mor and Gheini, Mozhdeh and T, Mukund Varma and Peng, Nanyun and Chi, Nathan and Lee, Nayeon and Krakover, Neta Gur-Ari and Cameron, Nicholas and Roberts, Nicholas and Doiron, Nick and Nangia, Nikita and Deckers, Niklas and Muennighoff, Niklas and Keskar, Nitish Shirish and Iyer, Niveditha S. and Constant, Noah and Fiedel, Noah and Wen, Nuan and Zhang, Oliver and Agha, Omar and Elbaghdadi, Omar and Levy, Omer and Evans, Owain and Casares, Pablo Antonio Moreno and Doshi, Parth and Fung, Pascale and Liang, Paul Pu and Vicol, Paul and Alipoormolabashi, Pegah and Liao, Peiyuan and Liang, Percy and Chang, Peter and Eckersley, Peter and Htut, Phu Mon and Hwang, Pinyu and Miłkowski, Piotr and Patil, Piyush and Pezeshkpour, Pouya and Oli, Priti and Mei, Qiaozhu and Lyu, Qing and Chen, Qinlang and Banjade, Rabin and Rudolph, Rachel Etta and Gabriel, Raefer and Habacker, Rahel and Delgado, Ramón Risco and Millière, Raphaël and Garg, Rhythm and Barnes, Richard and Saurous, Rif A. and Arakawa, Riku and Raymaekers, Robbe and Frank, Robert and Sikand, Rohan and Novak, Roman and Sitelew, Roman and LeBras, Ronan and Liu, Rosanne and Jacobs, Rowan and Zhang, Rui and Salakhutdinov, Ruslan and Chi, Ryan and Lee, Ryan and Stovall, Ryan and Teehan, Ryan and Yang, Rylan and Singh, Sahib and Mohammad, Saif M. and Anand, Sajant and Dillavou, Sam and Shleifer, Sam and Wiseman, Sam and Gruetter, Samuel and Bowman, Samuel R. and Schoenholz, Samuel S. and Han, Sanghyun and Kwatra, Sanjeev and Rous, Sarah A. and Ghazarian, Sarik and Ghosh, Sayan and Casey, Sean and Bischoff, Sebastian and Gehrmann, Sebastian and Schuster, Sebastian and Sadeghi, Sepideh and Hamdan, Shadi and Zhou, Sharon and Srivastava, Shashank and Shi, Sherry and Singh, Shikhar and Asaadi, Shima and Gu, Shixiang Shane and Pachchigar, Shubh and Toshniwal, Shubham and Upadhyay, Shyam and Shyamolima, {Debnath} and Shakeri, Siamak and Thormeyer, Simon and Melzi, Simone and Reddy, Siva and Makini, Sneha Priscilla and Lee, Soo-Hwan and Torene, Spencer and Hatwar, Sriharsha and Dehaene, Stanislas and Divic, Stefan and Ermon, Stefano and Biderman, Stella and Lin, Stephanie and Prasad, Stephen and Piantadosi, Steven T. and Shieber, Stuart M. and Misherghi, Summer and Kiritchenko, Svetlana and Mishra, Swaroop and Linzen, Tal and Schuster, Tal and Li, Tao and Yu, Tao and Ali, Tariq and Hashimoto, Tatsu and Wu, Te-Lin and Desbordes, Théo and Rothschild, Theodore and Phan, Thomas and Wang, Tianle and Nkinyili, Tiberius and Schick, Timo and Kornev, Timofei and Telleen-Lawton, Timothy and Tunduny, Titus and Gerstenberg, Tobias and Chang, Trenton and Neeraj, Trishala and Khot, Tushar and Shultz, Tyler and Shaham, Uri and Misra, Vedant and Demberg, Vera and Nyamai, Victoria and Raunak, Vikas and Ramasesh, Vinay and Prabhu, Vinay Uday and Padmakumar, Vishakh and Srikumar, Vivek and Fedus, William and Saunders, William and Zhang, William and Vossen, Wout and Ren, Xiang and Tong, Xiaoyu and Zhao, Xinran and Wu, Xinyi and Shen, Xudong and Yaghoobzadeh, Yadollah and Lakretz, Yair and Song, Yangqiu and Bahri, Yasaman and Choi, Yejin and Yang, Yichi and Hao, Yiding and Chen, Yifu and Belinkov, Yonatan and Hou, Yu and Hou, Yufang and Bai, Yuntao and Seid, Zachary and Zhao, Zhuoye and Wang, Zijian and Wang, Zijie J. and Wang, Zirui and Wu, Ziyi},
  
  keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), Computers and Society (cs.CY), Machine Learning (cs.LG), Machine Learning (stat.ML), FOS: Computer and information sciences, FOS: Computer and information sciences},
  
  title = {Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models},
  
  publisher = {arXiv},
  
  year = {2022},
  
  copyright = {arXiv.org perpetual, non-exclusive license}
}
@inproceedings{rudinger-script-lms,
    title = "Script Induction as Language Modeling",
    author = "Rudinger, Rachel  and
      Rastogi, Pushpendre  and
      Ferraro, Francis  and
      Van Durme, Benjamin",
    booktitle = "Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/D15-1195",
    doi = "10.18653/v1/D15-1195",
    pages = "1681--1686",
}
@inproceedings{jans-etal-2012-skip,
    title = "Skip N-grams and Ranking Functions for Predicting Script Events",
    author = "Jans, Bram  and
      Bethard, Steven  and
      Vuli{\'c}, Ivan  and
      Moens, Marie Francine",
    booktitle = "Proceedings of the 13th Conference of the {E}uropean Chapter of the Association for Computational Linguistics",
    month = apr,
    year = "2012",
    address = "Avignon, France",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/E12-1034",
    pages = "336--344",
}
@inproceedings{rudinger-etal-2015-learning,
    title = "Learning to predict script events from domain-specific text",
    author = "Rudinger, Rachel  and
      Demberg, Vera  and
      Modi, Ashutosh  and
      Van Durme, Benjamin  and
      Pinkal, Manfred",
    booktitle = "Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/S15-1024",
    doi = "10.18653/v1/S15-1024",
    pages = "205--210",
}
@inproceedings{lee-goldwasser-2019-multi,
    title = "Multi-Relational Script Learning for Discourse Relations",
    author = "Lee, I-Ta  and
      Goldwasser, Dan",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/P19-1413",
    doi = "10.18653/v1/P19-1413",
    pages = "4214--4226",
    abstract = "Modeling script knowledge can be useful for a wide range of NLP tasks. Current statistical script learning approaches embed the events, such that their relationships are indicated by their similarity in the embedding. While intuitive, these approaches fall short of representing nuanced relations, needed for downstream tasks. In this paper, we suggest to view learning event embedding as a multi-relational problem, which allows us to capture different aspects of event pairs. We model a rich set of event relations, such as Cause and Contrast, derived from the Penn Discourse Tree Bank. We evaluate our model on three types of tasks, the popular Mutli-Choice Narrative Cloze and its variants, several multi-relational prediction tasks, and a related downstream task{---}implicit discourse sense classification.",
}
@inproceedings{chambers-2017-behind,
    title = "Behind the Scenes of an Evolving Event Cloze Test",
    author = "Chambers, Nathanael",
    booktitle = "Proceedings of the 2nd Workshop on Linking Models of Lexical, Sentential and Discourse-level Semantics",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W17-0905",
    doi = "10.18653/v1/W17-0905",
    pages = "41--45",
    abstract = "This paper analyzes the narrative event cloze test and its recent evolution. The test removes one event from a document{'}s chain of events, and systems predict the missing event. Originally proposed to evaluate learned knowledge of event scenarios (e.g., scripts and frames), most recent work now builds ngram-like language models (LM) to beat the test. This paper argues that the test has slowly/unknowingly been altered to accommodate LMs.5 Most notably, tests are auto-generated rather than by hand, and no effort is taken to include core script events. Recent work is not clear on evaluation goals and contains contradictory results. We implement several models, and show that the test{'}s bias to high-frequency events explains the inconsistencies. We conclude with recommendations on how to return to the test{'}s original intent, and offer brief suggestions on a path forward.",
}@inproceedings{glucose,
    title = "{GLUCOSE}: {G}enera{L}ized and {CO}ntextualized Story Explanations",
    author = "Mostafazadeh, Nasrin  and
      Kalyanpur, Aditya  and
      Moon, Lori  and
      Buchanan, David  and
      Berkowitz, Lauren  and
      Biran, Or  and
      Chu-Carroll, Jennifer",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.emnlp-main.370",
    doi = "10.18653/v1/2020.emnlp-main.370",
    pages = "4569--4586",
    abstract = "When humans read or listen, they make implicit commonsense inferences that frame their understanding of what happened and why. As a step toward AI systems that can build similar mental models, we introduce GLUCOSE, a large-scale dataset of implicit commonsense causal knowledge, encoded as causal mini-theories about the world, each grounded in a narrative context. To construct GLUCOSE, we drew on cognitive psychology to identify ten dimensions of causal explanation, focusing on events, states, motivations, and emotions. Each GLUCOSE entry includes a story-specific causal statement paired with an inference rule generalized from the statement. This paper details two concrete contributions. First, we present our platform for effectively crowdsourcing GLUCOSE data at scale, which uses semi-structured templates to elicit causal explanations. Using this platform, we collected a total of {\textasciitilde}670K specific statements and general rules that capture implicit commonsense knowledge about everyday situations. Second, we show that existing knowledge resources and pretrained language models do not include or readily predict GLUCOSE{'}s rich inferential content. However, when state-of-the-art neural models are trained on this knowledge, they can start to make commonsense inferences on unseen stories that match humans{'} mental models.",
}
@inproceedings{knext-eval,
    title = "Evaluation of Commonsense Knowledge with {M}echanical {T}urk",
    author = "Gordon, Jonathan  and
      Van Durme, Benjamin  and
      Schubert, Lenhart",
    booktitle = "Proceedings of the {NAACL} {HLT} 2010 Workshop on Creating Speech and Language Data with {A}mazon{'}s Mechanical Turk",
    month = jun,
    year = "2010",
    address = "Los Angeles",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W10-0724",
    pages = "159--162",
}
@inproceedings{starsem-scripts,
    title = "What do Large Language Models Learn about Scripts?",
    author = "Sancheti, Abhilasha  and
      Rudinger, Rachel",
    booktitle = "Proceedings of the 11th Joint Conference on Lexical and Computational Semantics",
    month = jul,
    year = "2022",
    address = "Seattle, Washington",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.starsem-1.1",
    pages = "1--11",
    abstract = "Script Knowledge (Schank and Abelson, 1975) has long been recognized as crucial for language understanding as it can help in filling in unstated information in a narrative. However, such knowledge is expensive to produce manually and difficult to induce from text due to reporting bias (Gordon and Van Durme, 2013). In this work, we are interested in the scientific question of whether explicit script knowledge is present and accessible through pre-trained generative language models (LMs). To this end, we introduce the task of generating full event sequence descriptions (ESDs) given a scenario as a natural language prompt. Through zero-shot probing, we find that generative LMs produce poor ESDs with mostly omitted, irrelevant, repeated or misordered events. To address this, we propose a pipeline-based script induction framework (SIF) which can generate good quality ESDs for unseen scenarios (e.g., bake a cake). SIF is a two-staged framework that fine-tunes LM on a small set of ESD examples in the first stage. In the second stage, ESD generated for an unseen scenario is post-processed using RoBERTa-based models to filter irrelevant events, remove repetitions, and reorder the temporally misordered events. Through automatic and manual evaluations, we demonstrate that SIF yields substantial improvements (1-3 BLEU points) over a fine-tuned LM. However, manual analysis shows that there is great room for improvement, offering a new research direction for inducing script knowledge.",
}
@inproceedings{goal-oriented-scripts,
    title = "Goal-Oriented Script Construction",
    author = "Lyu, Qing  and
      Zhang, Li  and
      Callison-Burch, Chris",
    booktitle = "Proceedings of the 14th International Conference on Natural Language Generation",
    month = aug,
    year = "2021",
    address = "Aberdeen, Scotland, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.inlg-1.19",
    pages = "184--200",
    abstract = "The knowledge of scripts, common chains of events in stereotypical scenarios, is a valuable asset for task-oriented natural language understanding systems. We propose the Goal-Oriented Script Construction task, where a model produces a sequence of steps to accomplish a given goal. We pilot our task on the first multilingual script learning dataset supporting 18 languages collected from wikiHow, a website containing half a million how-to articles. For baselines, we consider both a generation-based approach using a language model and a retrieval-based approach by first retrieving the relevant steps from a large candidate pool and then ordering them. We show that our task is practical, feasible but challenging for state-of-the-art Transformer models, and that our methods can be readily deployed for various other datasets and domains with decent zero-shot performance.",
}
@inbook{len-situations-2000,
author = {Schubert, Lenhart K.},
title = {The Situations We Talk About},
year = {2000},
isbn = {0792372247},
publisher = {Kluwer Academic Publishers},
address = {USA},
abstract = {I argue in favor of associating situations (events, episodes, eventualities, etc.) with arbitrarily complex sentences, not just atomic predicates, in NL interpretation. In that respect, a Situation Semantics approach to incorporating situations into semantic representations is preferable to a Davidsonian one. However, I will further argue that beyond the notion of further or falsity of a sentence in a situation, as in Situation Semantics, we also need the notion of a sentence characterizing a situation, in order to deal adequately with causal relations mentioned or implied in NL texts. I propose a way of doing this that essentially reduces complex situations to joins of basic, Davidsonian ones, along with basic situations corresponding to negated predications. The resulting situational logic, called FOL**, captures many of the essential features of both Davidsonian and Situation Semantics approaches to representing the content of sentences describing situations. The proposed semantics supports common intuitions about truth-in-situations, about the existence of situations characterized by sentences, and about persistence of information from parts of situations to the whole. I allow for temporal parts of situations as well as concurrent parts, and distinguish persistence properties or telic and atelic sentences. The development of FOL** is part of a continuing effort to fully formalize Episodic Logic, an implemented knowledge representation designed to support language understanding.},
booktitle = {Logic-Based Artificial Intelligence},
pages = {407–439},
numpages = {33}
}
@article{jsbvgaes,
author = {Lane Lawley and Will Frey and Patrick Mullen and Alexander D. Wissner-Gross},
title ={Joint sparsity-biased variational graph autoencoders},
journal = {The Journal of Defense Modeling and Simulation},
published = {SAGE Publications},
volume = {18},
number = {3},
pages = {239-246},
month = mar,
year = {2021},
doi = {10.1177/1548512921996828},
URL = {https://doi.org/10.1177/1548512921996828},
eprint = {https://doi.org/10.1177/1548512921996828},
}
@inproceedings{lomeproto,
    title = "Logical Story Representations via {F}rame{N}et + Semantic Parsing",
    author = "Lawley, Lane  and
      Schubert, Lenhart",
    booktitle = "Proceedings of the Workshop on Dimensions of Meaning: Distributional and Curated Semantics (DistCurate 2022)",
    month = jul,
    year = "2022",
    address = "Seattle, Washington",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.distcurate-1.3",
    doi = "10.18653/v1/2022.distcurate-1.3",
    pages = "19--23",
}
@inproceedings{nesl,
    title = "Mining Logical Event Schemas From Pre-Trained Language Models",
    author = "Lawley, Lane  and
      Schubert, Lenhart",
    booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.acl-srw.25",
    doi = "10.18653/v1/2022.acl-srw.25",
    pages = "332--345",
}
@inproceedings{allennlp-coref,
    title = "End-to-end Neural Coreference Resolution",
    author = "Lee, Kenton  and
      He, Luheng  and
      Lewis, Mike  and
      Zettlemoyer, Luke",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/D17-1018",
    doi = "10.18653/v1/D17-1018",
    pages = "188--197",
    abstract = "We introduce the first end-to-end coreference resolution model and show that it significantly outperforms all previous work without using a syntactic parser or hand-engineered mention detector. The key idea is to directly consider all spans in a document as potential mentions and learn distributions over possible antecedents for each. The model computes span embeddings that combine context-dependent boundary representations with a head-finding attention mechanism. It is trained to maximize the marginal likelihood of gold antecedent spans from coreference clusters and is factored to enable aggressive pruning of potential mentions. Experiments demonstrate state-of-the-art performance, with a gain of 1.5 F1 on the OntoNotes benchmark and by 3.1 F1 using a 5-model ensemble, despite the fact that this is the first approach to be successfully trained with no external resources.",
}
@inproceedings{weber2022human,
  title={Human Schema Curation via Causal Association Rule Mining},
  author={Weber, Noah and Belyy, Anton and Holzenberger, Nils and Rudingerb, Rachel and Van Durmea, Benjamin},
  booktitle={Proceedings of the 16th Linguistic Annotation Workshop (LAW-XVI)@ LREC},
  pages={139--150},
  year={2022}
}
@article{proscript,
  title={proscript: Partially ordered scripts generation via pre-trained language models},
  author={Sakaguchi, Keisuke and Bhagavatula, Chandra and Bras, Ronan Le and Tandon, Niket and Clark, Peter and Choi, Yejin},
  journal={arXiv preprint arXiv:2104.08251},
  year={2021}
}
@article{lissa,
  title={Dialogue design and management for multi-session casual conversation with older adults},
  author={Razavi, S Zahra and Schubert, Lenhart K and Kane, Benjamin and Ali, Mohammad Rafayet and Van Orden, Kimberly and Ma, Tianyi},
  journal={arXiv preprint arXiv:1901.06620},
  year={2019}
}
@article{kane2022flexible,
  title={A Flexible Schema-Guided Dialogue Management Framework: From Friendly Peer to Virtual Standardized Cancer Patient},
  author={Kane, Benjamin and Giugno, Catherine and Schubert, Lenhart and Haut, Kurtis and Wohn, Caleb and Hoque, Ehsan},
  journal={arXiv preprint arXiv:2207.07276},
  year={2022}
}
@article{platonov2019spoken,
  title={A spoken dialogue system for spatial question answering in a physical blocks world},
  author={Platonov, Georgiy and Kane, Benjamin and Gindi, Aaron and Schubert, Lenhart K},
  journal={arXiv preprint arXiv:1911.02524},
  year={2019}
}