Skip to main navigation menu Skip to main content Skip to site footer

Vol. 3 (2015)

TACL approved

Semantic Parsing of Ambiguous Input through Paraphrasing and Verification

Published 2015-12-07

  • Philip Arthur
    • ,
  • Graham Neubig
    • ,
  • Sakriani Sakti
    • ,
  • Tomoki Toda
    • ,
  • Satoshi Nakamura

Philip Arthur
Nara Institute of Science and Technology

Graham Neubig
Nara Institute of Science and Technology

Sakriani Sakti
Nara Institute of Science and Technology

Tomoki Toda
Nara Institute of Science and Technology

Satoshi Nakamura
Nara Institute of Science and Technology

Abstract

We propose a new method for semantic parsing of ambiguous and ungrammatical input, such as search queries. We do so by building on an existing semantic parsing framework that uses synchronous context free grammars (SCFG) to jointly model the input sentence and output meaning representation. We generalize this SCFG framework to allow not one, but multiple outputs. Using this formalism, we construct a grammar that takes an ambiguous input string and jointly maps it into both a meaning representation and a natural language paraphrase that is less ambiguous than the original input. This paraphrase can beused to disambiguate the meaning representation via verification using a language model that calculates the probability of each paraphrase.
PDF (presented at NAACL 2016)

Author Biography

Philip Arthur

First year master student of Infomation Science Department.

Augmented Human Communication Laboratory

References

  1. Yoav Artzi and Luke Zettlemoyer. 2011. Bootstrapping semantic parsers from conversations. In Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 421–432.
  2. Yoshua Bengio, Ducharme Rejean, Pascal Vincent, and ´Christian Janvin. 2003. A neural probabilistic languagemodel. The Journal of Machine Learning Research, 3:1137–1155.
  3. Jonathan Berant and Percy Liang. 2014. Semantic parsingvia paraphrasing. In Proceedings of the 52th Annual Meeting of the Association for Computational Linguistics (ACL), pages 1415–1425.
  4. Kurt Bollacker. 2007. A platform for scalable, collaborative, structured information integration. In Proceedings of the 22nd Association ofr Advancement of Artificial Intelligence, pages 22–27.
  5. Chris Callison-Burch, Philipp Koehn, Christof Monz, and Omar F Zaidan. 2011. Findings of the 2011 workshop on statistical machine translation. In Proceedings of the Sixth Workshop on Statistical Machine Translation, pages 22–64.
  6. David Chiang. 2007. Hierarchical phrase-based translation. Computational Linguistics, (2):201–228.
  7. Anthony Fader, Luke Zettlemoyer, and Oren Etzioni. 2013. Paraphrase-driven learning for open question answering. In Proceedings of the 51th Annual Meeting of the Association for Computational Linguistics (ACL), pages 1608–1618.
  8. Michel Galley, Mark Hopkins, Kevin Knight, and Daniel Marcu. 2004. What’s in a translation rule? In Proceedings of the 2004 Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics (HLTNAACL), pages 273–280.
  9. Michel Galley, Jonathan Graehl, Kevin Knight, Daniel Marcu, Steve DeNeefe, Wei Wang, and Ignacio Thayer. 2006. Scalable inference and training of context-rich syntactic translation models. In Proceedings of the 44th Annual Meeting of the Association for Computational Linguistics (ACL), pages 961–968.
  10. Ruifang Ge and Raymond J Mooney. 2009. Learning a compositional semantic parser using an existing syntactic parser. In Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2-Volume 2, pages 611–619.
  11. Kenneth Heafield, Philipp Koehn, and Alon Lavie. 2013. Grouping language model boundary words to speed kbest extraction from hypergraphs. In Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 958–968.
  12. Kenneth Heafield. 2011. KenLM: faster and smaller language model queries. In Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 187–197.
  13. Philipp Koehn. 2004. Statistical significance tests for machine translation evaluation. In Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing (EMNLP).
  14. Tom Kwiatkowski, Eunsol Choi, Yoav Artzi, and Luke Zettlemoyer. 2013. Scaling semantic parsers with on-the-fly ontology matching. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1545–1556.
  15. Johannes Leveling. 2010. A comparative analysis: QA evaluation questions versus real-world queries. In Proceedings of the 7th International Conference on Language Resources and Evaluation (LREC).
  16. Peng Li, Yang Liu, and Maosong Sun. 2013. An extended GHKM algorithm for inducing lambda-SCFG. In Proceedings of the 27th Association for Advancement of Artificial Intelligence, pages 605–611.
  17. Percy Liang, Michael I. Jordan, and Dan Klein. 2011. Learning dependency-based compositional semantics. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics (ACL), pages 590–599.
  18. Kathleen R. McKeown. 1983. Paraphrasing questions using given and new information. Computational Linguistics, 9(1):1–10.
  19. Scott Miller, Robert Bobrow, Robert Ingria, and Richard Schwartz. 1994. Hidden understanding models of natural language. In Proceedings of the 32nd Annual Meeting of the Association for Computational Linguistics (ACL), pages 25–32.
  20. Graham Neubig, Taro Watanabe, Eiichiro Sumita, Shinsuke Mori, and Tatsuya Kawahara. 2011. An unsupervised model for joint phrase alignment and extraction.
  21. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (HLT-ACL), pages 632–641.
  22. Graham Neubig. 2013. Travatar: A forest-to-string machine translation engine based on tree transducers. In ACL (Conference System Demonstrations), pages 91– 96.
  23. Franz Josef Och and Hermann Ney. 2003. A systematic comparison of various statistical alignment models. Computational Linguistics, (1):19–51.
  24. Hoifung Poon and Pedro Domingos. 2009. Unsupervised semantic parsing. In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1–10.
  25. Hassan Sajjad, Patrick Pantel, and Michael Gamon. 2012. Underspecified query refinement via natural language question generation. In Proceedings of the 24th International Conference on Computational Linguistics (COLING), pages 2341–2356.
  26. Milad Shokouhi, Rosie Jones, Umut Ozertem, Karthik Raghunathan, and Fernando Diaz. 2014. Mobilequery reformulations. In Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval, pages 1011–1014.
  27. Chenguang Wang, Nan Duan, Ming Zhou, and Ming Zhang. 2013. Paraphrasing adaptation for web search ranking. In Proceedings of the 51th Annual Meeting of the Association for Computational Linguistics (ACL), pages 41–46.
  28. Yuk Wah Wong and Raymond J Mooney. 2006. Learning for semantic parsing with statistical machine translation. In Proceedings of the 2006 Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics (HLT-NAACL), pages 439–446.
  29. Yuk Wah Wong and Raymond J Mooney. 2007. Learning synchronous grammars for semantic parsing with lambda calculus. In Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics (ACL), number 1, pages 960–967.
  30. Xiaobing Xue and W. Bruce Croft. 2013. Modeling reformulation using query distributions. ACM Transaction on Information Systems, (2):6:1–6:34.
  31. John M Zelle and Raymond J Mooney. 1996. Learning to parse database queries using inductive logic programming. In Proceedings of the 13th National Conference on Artificial Intelligence, pages 1050–1055.
  32. Luke S Zettlemoyer and Michael Collins. 2005. Learning to map sentences to logical form: Structured classification with probabilistic categorial grammars. Uncertainty in Artificial Intelligence (UAI), pages 658–666.
  33. Luke S Zettlemoyer and Michael Collins. 2007. Online learning of relaxed CCG grammars for parsing to logical form. In Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL), pages 678–687.