Hostname: page-component-cd9895bd7-p9bg8 Total loading time: 0 Render date: 2024-12-23T19:00:36.485Z Has data issue: false hasContentIssue false
  • English
  • Français

Using Answer Set Programming for Commonsense Reasoning in the Winograd Schema Challenge

Published online by Cambridge University Press:  20 September 2019

ARPIT SHARMA Open the ORCID record for ARPIT SHARMA [Opens in a new window]
ARPIT SHARMA*
Affiliation:
Arizona State University, Tempe, USA (e-mail: [email protected])
Get access Rights & Permissions [Opens in a new window]

Abstract

The Winograd Schema Challenge (WSC) is a natural language understanding task proposed as an alternative to the Turing test in 2011. In this work we attempt to solve WSC problems by reasoning with additional knowledge. By using an approach built on top of graph-subgraph isomorphism encoded using Answer Set Programming (ASP) we were able to handle 240 out of 291 WSC problems. The ASP encoding allows us to add additional constraints in an elaboration tolerant manner. In the process we present a graph based representation of WSC problems as well as relevant commonsense knowledge.

Keywords

Answer Set ProgrammingWinograd Schema ChallengeCommonsense ReasoningGraph-Subgraph Isomorphism
Type
Original Article
Information
Theory and Practice of Logic Programming , Volume 19 , Special Issue 5-6: 35th International Conference on Logic Programming , September 2019 , pp. 1021 - 1037
Copyright
© Cambridge University Press 2019 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Bailey, D., Harrison, A., Lierler, Y., Lifschitz, V., and Michael, J. 2015. The winograd schema challenge and reasoning about correlation. In In Working Notes of the Symposium on Logical Formalizations of Commonsense Reasoning.Google Scholar
Banarescu, L., Bonial, C., Cai, S., Georgescu, M., Griffitt, K., Hermjakob, U., Knight, K., Koehn, P., Palmer, M., and Schneider, N. 2013. Abstract meaning representation for sembanking. In Proceedings of the 7th Linguistic Annotation Workshop and Interoperability with Discourse. 178–186.Google Scholar
Baral, C. 2003. Knowledge representation, reasoning and declarative problem solving. Cambridge university press.Google Scholar
Cordella, L. P., Foggia, P., Sansone, C., and Vento, M. 2004. A (sub) graph isomorphism algorithm for matching large graphs. IEEE transactions on pattern analysis and machine intelligence 26, 10, 1367–1372.CrossRefGoogle Scholar
Emami, A., De La Cruz, N., Trischler, A., Suleman, K., and Cheung, J. C. K. 2018. A knowledge hunting framework for common sense reasoning. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. 1949–1958.Google Scholar
Gelfond, M. and Lifschitz, V. 1988. The stable model semantics for logic programming. In ICLP/SLP. Vol. 88. 1070–1080.Google Scholar
Isaak, N. and Michael, L. 2016. Tackling the winograd schema challenge through machine logical inferences. In STAIRS. Vol. 284. 75–86.Google Scholar
Levesque, H. J., Davis, E., and Morgenstern, L. 2011. The winograd schema challenge. In AAAI Spring Symposium: Logical Formalizations of Commonsense Reasoning. Vol. 46. 47.Google Scholar
Lin, F. and Zhao, Y. 2004. Assat: Computing answer sets of a logic program by sat solvers. Artificial Intelligence 157, 1-2, 115–137.CrossRefGoogle Scholar
Liu, Q., Jiang, H., Evdokimov, A., Ling, Z.-H., Zhu, X., Wei, S., and Hu, Y. 2017. Cause-effect knowledge acquisition and neural association model for solving a set of winograd schema problems. In Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI). 2344–2350.Google Scholar
Miller, G. A. 1995. Wordnet: a lexical database for english. Communications of the ACM 38, 11, 39–41.Google Scholar
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., and Sutskever, I. 2019. Language models are unsupervised multitask learners. OpenAI Blog 1, 8.Google Scholar
Richard-Bollans, A., Gomez Alvarez, L., and Cohn, A. G. 2018. The role of pragmatics in solving the winograd schema challenge. In Proceedings of the Thirteenth International Symposium on Commonsense Reasoning (Commonsense 2017). CEUR Workshop Proceedings.Google Scholar
Schüller, P. 2014. Tackling winograd schemas by formalizing relevance theory in knowledge graphs. In Fourteenth International Conference on the Principles of Knowledge Representation and Reasoning.Google Scholar
Sharma, A., Vo, N., Aditya, S., and Baral, C. 2015a. Identifying various kinds of event mentions in k-parser output. In Proceedings of the The 3rd Workshop on EVENTS: Definition, Detection, Coreference, and Representation. 82–88.Google Scholar
Sharma, A., Vo, N. H., Aditya, S., and Baral, C. 2015b. Towards addressing the winograd schema challenge-building and using a semantic parser and a knowledge hunting module. In IJCAI. 1319–1325.Google Scholar
Wolff, J. G. 2018. Interpreting winograd schemas via the sp theory of intelligence and its realisation in the sp computer model. arXiv preprint arXiv:1810.04554.Google Scholar
Supplementary material: PDF

Sharma supplementary material

Appendix

Download Sharma supplementary material(PDF)
PDF 201.2 KB