Learning Context-Dependent Mappings from Sentences to Logical Form
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Collins_Learning context.pdf
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191.6 KB
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Adobe PDF
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Author(s)
Zettlemoyer, Luke S.
Collins, Michael
Date Issued
January 2009
Journal
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
Publisher
Association for Computing Machinery
Citation
Zettlemoyer, Luke S., and Michael Collins. “Learning context-dependent mappings from sentences to logical form.” 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, Suntec, Singapore: Association for Computational Linguistics, 2009. 976-984.
Version
Author's final manuscript
Abstract
We consider the problem of learning context-dependent mappings from sentences to logical form. The training examples are sequences of sentences annotated with lambda-calculus meaning representations. We develop an algorithm that maintains explicit, lambda-calculus representations of salient discourse entities and uses a context-dependent analysis pipeline to recover logical forms. The method uses a hidden-variable variant of the perception algorithm to learn a linear model used to select the best analysis. Experiments on context-dependent utterances from the ATIS corpus show that the method recovers fully correct logical forms with 83.7% accuracy.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
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Attribution-Noncommercial-Share Alike 3.0 Unported
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