The defeat of the Winograd Schema Challenge
Tài liệu tham khảo
Amsili, 2017, A Google-proof collection of French Winograd schemas
Bakhtin, 2022, Human-level play in the game of diplomacy by combining language models with strategic reasoning, Science, 378, 1067, 10.1126/science.ade9097
D. Bender, Establishing a human baseline for the Winograd Schema Challenge, 2015.
Bernard, 2020, Mandarinograd: a Chinese collection of Winograd schemas
Brown, 2020, Language models are few-shot learners
2006
Charniak, 1972
Chinchor, 1998
Cozman, 2020, The Winograd schemas from hell, 531
Davis, 2013, Qualitative spatial reasoning in interpreting text and narrative, Spat. Cogn. Comput., 10.1080/13875868.2013.824976
Davis, 2021, Using human skills taxonomies and tests in as measures of artificial intelligence
Davis
Davis, 2015, Commonsense reasoning and commonsense knowledge in artificial intelligence, Commun. ACM, 58, 92, 10.1145/2701413
Davis, 2017, Commonsense reasoning about containers using radically incomplete information, Artif. Intell., 248, 46, 10.1016/j.artint.2017.03.004
Davis
Davis, 2017, The first Winograd Schema Challenge at IJCAI-16, AI Mag.
Devlin, 2019, BERT: pre-training of deep bidirectional transformers for language understanding
Elazar
Emami, 2018, A knowledge hunting framework for common sense reasoning
Emelin, 2021, Wino-X: multilingual Winograd schemas for commonsense reasoning and coreference resolution, 8517
Fähndrich, 2018, A marker passing approach to Winograd schemas
Grishman, 1996
Grosz, 1977, The representation and use of focus in a system for understanding dialogs, 67
Gunning
Hansson, 2021, The Swedish Winogender database, 452
He, 2019, A hybrid neural network model for commonsense reasoning
He, 2021, WinoLogic: a zero-shot logic-based diagnostic dataset for Winograd schema challenge, 3779
Hobbs, 1979, Coherence and coreference, Cogn. Sci., 3, 67, 10.1207/s15516709cog0301_4
Hobbs, 1993, Interpretation as abduction, Artif. Intell., 63, 69, 10.1016/0004-3702(93)90015-4
Hong
Isaak, 2016, Tackling the Winograd Schema Challenge through machine logical inferences
Isaak, 2019, Winoflexi: a crowdsourcing platform for the development of Winograd schemas
Isaak, 2020, Winventor: a machine-driven approach for the development of Winograd schemas, vol. 2, 26
Kahneman, 2011
Kakwani, 2020, Inlpsuite: monolingual corpora, evaluation benchmarks and pre-trained multilingual language models for Indian languages, 4948
Kameyama, 1986, A property-sharing constraint in centering, 200
A. Kehler, Testing for common sense: Thoughts on pronoun interpretation and the Winograd schema challenge, Talk presented at the Workshop on Language & Common Sense: Integrating Across Psychology, Linguistics, and Computer Science, CogSci-2015, 2015.
Kehler, 2008, Coherence and coreference revisited, J. Semant., 25, 1, 10.1093/jos/ffm018
Khashabi
Klein, 2019, Attention is (not) all you need for commonsense reasoning
Knight, 2016, Tougher Turing test exposes chatbots' stupidity, Technol. Rev.
Kocijan, 2019, WikiCREM: a large unsupervised corpus for coreference resolution
Kocijan, 2019, A surprisingly robust trick for Winograd schema challenge
Kocijan
Kocmi, 2020, Gender coreference and bias evaluation at WMT 2020, 357
Lake
H.J. Levesque, The Winograd Schema Challenge, AAAI Spring Symposium: Logical Formalizations of Commonsense Reasoning, 2011.
Levesque, 2014, On our best behaviour, Artif. Intell., 10.1016/j.artint.2014.03.007
Levesque, 2017
Levesque, 2012, The Winograd Schema Challenge
Lin
Linzen
Liu, 2020, Precise task formalization matters in Winograd schema evaluations
Liu, 2017, Cause-effect knowledge acquisition and neural association model for solving a set of Winograd Schema Problems
Liu, 2017
Liu
Lourie, 2021, UNICORN on RAINBOW: a universal commonsense reasoning model on a new multitask benchmark
Marcus, 2020, GPT-3, Bloviator: OpenAI's language generator has no idea what it's talking about, Technol. Rev.
Markoff, 2015
McDermott, 1976, Artificial intelligence meets natural stupidity, ACM SIGART Bull., 4, 10.1145/1045339.1045340
Melo, 2020, Esquemas de Winograd em português
Mikolov, 2013, Distributed Representations of Words and Phrases and Their Compositionality
Morgenstern, 2021, Technical perspective: the importance of WINOGRANDE, Commun. ACM, 64, 98, 10.1145/3474378
Morgenstern, 2016, Planning, executing, and evaluating the Winograd Schema Challenge, AI Mag., 37, 50
Nangia
Opitz, 2018, Addressing the Winograd Schema Challenge as a sequence ranking task
Peng, 2015, Solving hard co-reference problems
Poesio
Poesio, 2016
Prakash, 2019, Combining knowledge hunting and neural language models to solve the Winograd schema challenge
A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, I. Sutskever, Language models are unsupervised multitask learners, 2019.
Rahman, 2012, Resolving complex cases of definite pronouns: the Winograd Schema Challenge
Rajpurkar
Rohde, 2018, Pronoun interpretation and production
Ruan
Rudinger, 2018, Gender bias in coreference resolution
Sakaguchi, 2020, WINOGRANDE: an adversarial Winograd Schema Challenge at scale
Sakaguchi, 2021, Winogrande: an adversarial Winograd schema challenge at scale, Commun. ACM, 64, 99, 10.1145/3474381
Schank, 1977
Sharma
Sharma, 2015, Towards addressing the Winograd Schema Challenge – building and using a semantic parser and a knowledge hunting module
Shavrina
Sidner, 1979
Stanovsky, 2019, Evaluating gender bias in machine translation
Storks
Thrush, 2022, Probing vision and language models for visio-linguistic compositionality, 5238
Trichelair, 2018, On the evaluation of common-sense reasoning in natural language understanding
Trinh
Vadász, 2022, Winograd schemata and other datasets for anaphora resolution in Hungarian, Acta Linguist. Acad.
Wang
Wang, 2019, GLUE: a multi-task benchmark and analysis platform for natural language understanding
Wang, 2019, Unsupervised deep structured semantic models for commonsense reasoning
Wilks, 1975, An intelligent analyzer and understander of English, Commun. ACM, 18, 264, 10.1145/360762.360770
Winograd, 1972
Wolf, 2004, Discourse coherence and pronoun resolution, Lang. Cogn. Processes, 19, 665, 10.1080/01690960444000034
Xu
Yang, 2020, Generative data augmentation for commonsense reasoning
Ye
Yordanov, 2020, Does the objective matter? Comparing training objectives for pronoun resolution
Yordanov
Žagar
Zhang, 2018, A Distributed Solution for Winograd Schema Challenge, 10.1145/3195106.3195127
Zhang, 2020, A deep diagnosis of essential commonsense knowledge for answering Winograd schema challenge
Zhao, 2018, Gender bias in coreference resolution: evaluation and debiasing methods