Semantic Parsing with Bayesian Tree Transducers

Bevan Jones, Mark Johnson, Sharon Goldwater

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

Abstract / Description of output

Many semantic parsing models use tree transformations to map between natural language and meaning representation. However, while tree transformations are central to several state-of-the-art approaches, little use has been made of the rich literature on tree automata. This paper makes the connection concrete with a tree transducer based semantic parsing model and suggests that other models can be interpreted in a similar framework, increasing the generality of their contributions. In particular, this paper further introduces a variational Bayesian inference algorithm that is applicable
to a wide class of tree transducers, producing state-of-the-art semantic parsing results while remaining applicable to any domain employing probabilistic tree transducers.
Original languageEnglish
Title of host publicationProceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Place of PublicationJeju Island, Korea
PublisherAssociation for Computational Linguistics
Number of pages9
Publication statusPublished - 1 Jul 2012

Keywords / Materials (for Non-textual outputs)

  • @inf-nlp


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