Semantic Parsing with Semi-Supervised Sequential Autoencoders

We present a novel semi-supervised approach for sequence transduction and apply it to semantic parsing. The unsupervised component is based on a generative model in which latent sentences generate the unpaired logical forms. We apply this method to a number of semantic parsing tasks focusing on domains with limited access to labelled training data and extend those datasets with synthetically generated logical forms.

• Publication year

  2016

• Venue

  Conference on Empirical Methods in Natural Language Processing

• Publication date

  2016-09-29

• Fields of study

  Computer Science

• Identifiers
  DOI 10.18653/v1/D16-1116arXiv 1609.09315
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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