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Semantic parsing with semi-supervised sequential autoencoders

Abstract:
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 status:
Published
Peer review status:
Peer reviewed

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Institution:
University of Oxford
Oxford college:
Linacre College
Role:
Author
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Publisher:
Association for Computational Linguistics Publisher's website
Journal:
Empirical Methods in Natural Language Processing Conference 2016 Journal website
Pages:
1078-1087
Host title:
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing
Publication date:
2016-11-01
Acceptance date:
2016-07-29
Source identifiers:
648418
Keywords:
Pubs id:
pubs:648418
UUID:
uuid:071abf74-decf-421c-96e4-2b8724967fac
Local pid:
pubs:648418
Deposit date:
2016-10-16

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