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Winograd schema challenge

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126:, which involves free-flowing, unrestricted conversations in English between human judges and computer programs over a text-only channel (such as teletype). In general, the machine passes the test if interrogators are not able to tell the difference between it and a human in a five-minute conversation. 936:
Brown, Tom B.; Mann, Benjamin; Ryder, Nick; Subbiah, Melanie; Kaplan, Jared; Dhariwal, Prafulla; Neelakantan, Arvind; Shyam, Pranav; Sastry, Girish; Askell, Amanda; Agarwal, Sandhini; Herbert-Voss, Ariel; Krueger, Gretchen; Henighan, Tom; Child, Rewon; Ramesh, Aditya; Ziegler, Daniel M.; Wu, Jeffrey;
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The 2016 Winograd Schema Challenge was run on July 11, 2016 at IJCAI-16. There were four contestants. The first round of the contest was to solve PDPs—pronoun disambiguation problems, adapted from literary sources, not constructed as pairs of sentences. The highest score achieved was 58% correct, by
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The schema challenge question is, "Does the pronoun 'they' refer to the city councilmen or the demonstrators?" Switching between the two instances of the schema changes the answer. The answer is immediate for a human reader, but proves difficult to emulate in machines. Levesque argues that knowledge
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achieved 70% accuracy on 70 manually selected problems from the original 273 Winograd schema dataset. In June 2018, a score of 63.7% accuracy was achieved on the full dataset using an ensemble of recurrent neural network language models, marking the first use of deep neural networks that learn from
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In 2016 and 2018, Nuance Communications sponsored a competition, offering a grand prize of $ 25,000 for the top scorer above 90% (for comparison, humans correctly answer to 92–96% of WSC questions). However, nobody came close to winning the prize in 2016 and the 2018 competition was cancelled for
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The key factor in the WSC is the special format of its questions, which are derived from Winograd schemas. Questions of this form may be tailored to require knowledge and commonsense reasoning in a variety of domains. They must also be carefully written not to betray their answers by
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One difficulty with the Winograd schema challenge is the development of the questions. They need to be carefully tailored to ensure that they require commonsense reasoning to solve. For example, Levesque gives the following example of a so-called Winograd schema that is "too easy":
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Quan Liu et al, of the University of Science and Technology, China. Hence, by the rules of that challenge, no prizes were awarded, and the challenge did not proceed to the second round. The organizing committee in 2016 was Leora Morgenstern, Ernest Davis, and Charles Ortiz.
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Winter, Clemens; Hesse, Christopher; Chen, Mark; Sigler, Eric; Litwin, Mateusz; Gray, Scott; Chess, Benjamin; Clark, Jack; Berner, Christopher; McCandlish, Sam; Radford, Alec; et al. (2020). "Language Models are Few-Shot Learners".
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The prize could not be awarded to anybody. Most of the participants showed a result close to the random choice or even worse. The second competition scheduled for 2018 was canceled due to the lack of prospective
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A more challenging, adversarial "Winogrande" dataset of 44,000 problems was designed in 2019. This dataset consists of fill-in-the-blank style sentences, as opposed to the pronoun format of previous datasets.
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Liu, Quan; Jiang, Hui; Ling, Zhen-Hua; Zhu, Xiaodan; Wei, Si; Hu, Yu (2016). "Commonsense Knowledge Enhanced Embeddings for Solving Pronoun Disambiguation Problems in Winograd Schema Challenge".
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announced in July 2014 that it would sponsor an annual WSC competition, with a prize of $ 25,000 for the best system that could match human performance. However, the prize is no longer offered.
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claimed to pass it in 2014. One of the major concerns with the Turing test is that a machine could easily pass the test with brute force and/or trickery, rather than true intelligence.
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plays a central role in these problems: the answer to this schema has to do with our understanding of the typical relationships between and behavior of councilmen and demonstrators.
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The Winograd schema challenge was proposed in 2012 in part to ameliorate the problems that came to light with the nature of the programs that performed well on the test.
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independent corpora to acquire common sense knowledge. In 2019 a score of 90.1%, was achieved on the original Winograd schema dataset by fine-tuning of the
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Spring Symposium Series at Stanford University, with a special focus on the Winograd schema challenge. The organizing committee included Leora Morgenstern (
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Conversation: A lot of interaction may qualify as "legitimate conversation"—jokes, clever asides, points of order—without requiring intelligent reasoning.
50:, it is a multiple-choice test that employs questions of a very specific structure: they are instances of what are called Winograd schemas, named after 267:
Winograd schemas of varying difficulty may be designed, involving anything from simple cause-and-effect relationships to complex narratives of events.
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A special word and alternate word, such that if the special word is replaced with the alternate word, the natural resolution of the pronoun changes.
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The performance of Eugene Goostman exhibited some of the Turing test's problems. Levesque identifies several major issues, summarized as follows:
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Sakaguchi, Keisuke; Le Bras, Ronan; Bhagavatula, Chandra; Choi, Yejin (2019). "WinoGrande: An Adversarial Winograd Schema Challenge at Scale".
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language model with appropriate WSC-like training data to avoid having to learn commonsense reasoning. The general language model
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The Twelfth International Symposium on the Logical Formalizations of Commonsense Reasoning was held on March 23–25, 2015 at the
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They may be constructed to test reasoning ability in specific domains (e.g., social/psychological or spatial reasoning).
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A machine will be given the problem in a standardized form which includes the answer choices, thus making it a
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Proceedings of the Thirteenth International Conference on Principles of Knowledge Representation and Reasoning
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Boguslavsky, I.M.; Frolova, T.I.; Iomdin, L.L.; Lazursky, A.V.; Rygaev, I.P.; Timoshenko, S.P. (2019).
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Proceedings of the International Conference of Computational Linguistics and Intellectual Technologies
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Kocijan, Vid; Davis, Ernest; Lukasiewicz, Thomas; Marcus, Gary; Morgenstern, Leora (11 July 2023).
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Deception: The machine is forced to construct a false identity, which is not part of intelligence.
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Liu, Quan; Jiang, Hui; Evdokimov, Andrew; Ling, Zhen-Hua; Zhu, Xiaodan; Wei, Si; Hu, Yu (2017).
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Trinh, Trieu H.; Le, Quoc V. (26 September 2019). "A Simple Method for Commonsense Reasoning".
577:"Nuance announces the Winograd Schemas Challenge to Advance Artificial Intelligence Innovation" 62: 1006: 770: 339: 129: 78: 43: 322:), Theodore Patkos (The Foundation for Research & Technology Hellas), and Robert Sloan ( 77:, but Levesque argues that for Winograd schemas, the task requires the use of knowledge and 199:
Since the original proposal of the Winograd schema challenge, Ernest Davis, a professor at
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Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
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The city councilmen refused the demonstrators a permit because they advocated violence.
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The first cited example of a Winograd schema (and the reason for their name) is due to
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The city councilmen refused the demonstrators a permit because they feared violence.
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The choices of "feared" and "advocated" turn the schema into its two instances:
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Evaluation: Humans make mistakes and judges often would disagree on the results.
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The Theory of Correlation Formulas and Their Application to Discourse Coherence
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The city councilmen refused the demonstrators a permit because they violence.
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The women stopped taking pills because they were . Which individuals were ?
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On the surface, Winograd schema questions simply require the resolution of
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The Winograd schema challenge has the following purported advantages:
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A version of the Winograd schema challenge is one part of the GLUE (
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Morgenstern, Leora; Davis, Ernest; Ortiz, Charles L. (March 2016).
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in 1950, the Turing test plays a central role in the philosophy of
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Two answer choices corresponding to the noun phrases in question.
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achieved a score of 88.3% without specific fine-tuning in 2020.
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The Winograd Schema Challenge was proposed in the spirit of the
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Davis, Ernest; Morgenstern, Leora; Ortiz, Charles (Fall 2017).
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Knowledge and commonsense reasoning are required to solve them.
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The challenge is considered defeated in 2019 since a number of
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The answer to this question can be determined on the basis of
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A Winograd schema challenge question consists of three parts:
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Levesque, Hector; Davis, Ernest; Morgenstern, Leora (2012).
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A question asking the identity of the ambiguous pronoun, and
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or statistical information about the words in the sentence.
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A sentence or brief discourse that contains the following:
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Website for the contest sponsored by Nuance Communications
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Association for the Advancement of Artificial Intelligence
226:(male, female, inanimate, or group of objects or people), 554: 38:) is a test of machine intelligence proposed in 2012 by 721:"Knowledge-based approach to Winograd Schema Challenge" 233:
that may refer to either of the above noun phrases, and
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Designed to be an improvement on the 14: 999: 509:"Computing Machinery and Intelligence" 503: 569: 543: 206: 763: 154: 54:, professor of computer science at 24: 738: 668:"A Collection of Winograd Schemas" 618: 273:There is no need for human judges. 25: 1028: 980: 665: 335:commonsense knowledge acquisition 324:University of Illinois at Chicago 92:achieved accuracies of over 90%. 992:https://arxiv.org/abs/2201.02387 627:"Understanding Natural Language" 625:Winograd, Terry (January 1972). 65:: the machine must identify the 951: 908: 869: 828: 807: 788: 712: 602:Michael, Julian (18 May 2015). 378:Ackerman, Evan (29 July 2014). 437: 359:natural-language understanding 42:, a computer scientist at the 13: 1: 558:The Winograd Schema Challenge 364: 255: 136:Weaknesses of the Turing test 646:10.1016/0010-0285(72)90002-3 472:10.1016/j.artint.2023.103971 430:10.1016/j.artint.2014.03.007 27:Test of machine intelligence 7: 771:"AAAI 2015 Spring Symposia" 746:"Winograd Schema Challenge" 305: 277: 75:natural language processing 10: 1033: 95: 1017:Word-sense disambiguation 167: 32:Winograd schema challenge 18:Winograd Schema Challenge 854:10.1609/aimag.v37i1.2639 750:CommonsenseReasoning.org 530:10.1093/mind/LIX.236.433 409:Levesque, H. 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(2014). 293:selectional restrictions 162:selectional restrictions 891:10.24963/ijcai.2017/326 450:Artificial Intelligence 416:Artificial Intelligence 411:"On our best behaviour" 110:artificial intelligence 884:. pp. 2344–2350. 130:Nuance Communications 79:commonsense reasoning 44:University of Toronto 634:Cognitive Psychology 201:New York University 56:Stanford University 207:Formal description 963:GlueBenchmark.com 16:(Redirected from 1024: 974: 973: 971: 969: 959:"GLUE Benchmark" 955: 949: 948: 946: 933: 927: 926: 924: 912: 906: 905: 893: 873: 867: 866: 856: 832: 826: 825: 823: 811: 805: 804: 792: 786: 785: 783: 781: 767: 761: 760: 758: 756: 742: 736: 735: 725: 716: 710: 709: 707: 695: 684: 683: 681: 679: 663: 657: 656: 654: 652: 631: 622: 616: 615: 599: 593: 592: 590: 588: 573: 567: 566: 552: 541: 540: 538: 536: 524:(236): 433–460. 513: 507:(October 1950). 501: 492: 491: 465: 441: 435: 434: 432: 406: 395: 394: 392: 390: 375: 155:Winograd schemas 69:of an ambiguous 21: 1032: 1031: 1027: 1026: 1025: 1023: 1022: 1021: 997: 996: 983: 978: 977: 967: 965: 957: 956: 952: 934: 930: 913: 909: 902: 874: 870: 833: 829: 812: 808: 793: 789: 779: 777: 769: 768: 764: 754: 752: 744: 743: 739: 723: 717: 713: 696: 687: 677: 675: 666:Davis, Ernest. 664: 660: 650: 648: 629: 623: 619: 600: 596: 586: 584: 575: 574: 570: 553: 544: 534: 532: 511: 502: 495: 442: 438: 407: 398: 388: 386: 376: 372: 367: 308: 289: 280: 258: 250:binary decision 209: 193: 182: 170: 157: 138: 114:Eugene Goostman 98: 90:language models 40:Hector Levesque 28: 23: 22: 15: 12: 11: 5: 1030: 1020: 1019: 1014: 1009: 995: 994: 989: 982: 981:External links 979: 976: 975: 950: 928: 907: 900: 868: 827: 806: 787: 762: 737: 711: 685: 658: 617: 594: 583:. 28 July 2014 568: 542: 493: 436: 396: 369: 368: 366: 363: 307: 304: 285: 279: 276: 275: 274: 271: 268: 265: 257: 254: 246: 245: 242: 239: 238: 237: 234: 227: 224:semantic class 208: 205: 186: 178: 174:Terry Winograd 169: 166: 156: 153: 152: 151: 148: 145: 137: 134: 124:imitation game 104:. 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Index

Winograd Schema Challenge
Hector Levesque
University of Toronto
Turing test
Terry Winograd
Stanford University
anaphora
antecedent
pronoun
natural language processing
commonsense reasoning
transformer
language models
Turing test
Alan Turing
artificial intelligence
Eugene Goostman
imitation game
Nuance Communications
selectional restrictions
Terry Winograd
New York University
noun phrases
semantic class
pronoun
binary decision
selectional restrictions
AAAI
Leidos
University of Illinois at Chicago

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