Driving semantic parsing from the world's response

James Clarke, Dan Goldwasser, Ming Wei Chang, Dan Roth

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Current approaches to semantic parsing, the task of converting text to a formal meaning representation, rely on annotated training data mapping sentences to logical forms. Providing this supervision is a major bottleneck in scaling semantic parsers. This paper presents a new learning paradigm aimed at alleviating the supervision burden. We develop two novel learning algorithms capable of predicting complex structures which only rely on a binary feedback signal based on the context of an external world. In addition we reformulate the semantic parsing problem to reduce the dependency of the model on syntactic patterns, thus allowing our parser to scale better using less supervision. Our results surprisingly show that without using any annotated meaning representations learning with a weak feedback signal is capable of producing a parser that is competitive with fully supervised parsers.

Original languageEnglish (US)
Title of host publicationCoNLL 2010 - Fourteenth Conference on Computational Natural Language Learning, Proceedings of the Conference
Pages18-27
Number of pages10
StatePublished - Dec 1 2010
Event14th Conference on Computational Natural Language Learning, CoNLL 2010 - Uppsala, Sweden
Duration: Jul 15 2010Jul 16 2010

Publication series

NameCoNLL 2010 - Fourteenth Conference on Computational Natural Language Learning, Proceedings of the Conference

Other

Other14th Conference on Computational Natural Language Learning, CoNLL 2010
CountrySweden
CityUppsala
Period7/15/107/16/10

    Fingerprint

ASJC Scopus subject areas

  • Artificial Intelligence
  • Human-Computer Interaction
  • Linguistics and Language

Cite this

Clarke, J., Goldwasser, D., Chang, M. W., & Roth, D. (2010). Driving semantic parsing from the world's response. In CoNLL 2010 - Fourteenth Conference on Computational Natural Language Learning, Proceedings of the Conference (pp. 18-27). (CoNLL 2010 - Fourteenth Conference on Computational Natural Language Learning, Proceedings of the Conference).