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        <title>Decoupling Structure and Lexicon for Zero-Shot Semantic Parsing</title>
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        <namePart type="given">Jonathan</namePart>
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        <namePart type="family">Berant</namePart>
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            <title>Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing</title>
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        <name type="personal">
            <namePart type="given">Ellen</namePart>
            <namePart type="family">Riloff</namePart>
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        <name type="personal">
            <namePart type="given">David</namePart>
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        <name type="personal">
            <namePart type="given">Julia</namePart>
            <namePart type="family">Hockenmaier</namePart>
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        <name type="personal">
            <namePart type="given">Jun’ichi</namePart>
            <namePart type="family">Tsujii</namePart>
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    <abstract>Building a semantic parser quickly in a new domain is a fundamental challenge for conversational interfaces, as current semantic parsers require expensive supervision and lack the ability to generalize to new domains. In this paper, we introduce a zero-shot approach to semantic parsing that can parse utterances in unseen domains while only being trained on examples in other source domains. First, we map an utterance to an abstract, domain independent, logical form that represents the structure of the logical form, but contains slots instead of KB constants. Then, we replace slots with KB constants via lexical alignment scores and global inference. Our model reaches an average accuracy of 53.4% on 7 domains in the OVERNIGHT dataset, substantially better than other zero-shot baselines, and performs as good as a parser trained on over 30% of the target domain examples.</abstract>
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    <identifier type="doi">10.18653/v1/D18-1190</identifier>
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        <date>2018-oct-nov</date>
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%0 Conference Proceedings
%T Decoupling Structure and Lexicon for Zero-Shot Semantic Parsing
%A Herzig, Jonathan
%A Berant, Jonathan
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F herzig-berant-2018-decoupling
%X Building a semantic parser quickly in a new domain is a fundamental challenge for conversational interfaces, as current semantic parsers require expensive supervision and lack the ability to generalize to new domains. In this paper, we introduce a zero-shot approach to semantic parsing that can parse utterances in unseen domains while only being trained on examples in other source domains. First, we map an utterance to an abstract, domain independent, logical form that represents the structure of the logical form, but contains slots instead of KB constants. Then, we replace slots with KB constants via lexical alignment scores and global inference. Our model reaches an average accuracy of 53.4% on 7 domains in the OVERNIGHT dataset, substantially better than other zero-shot baselines, and performs as good as a parser trained on over 30% of the target domain examples.
%R 10.18653/v1/D18-1190
%U https://aclanthology.org/D18-1190
%U https://doi.org/10.18653/v1/D18-1190
%P 1619-1629

Markdown (Informal)

[Decoupling Structure and Lexicon for Zero-Shot Semantic Parsing](https://aclanthology.org/D18-1190) (Herzig & Berant, EMNLP 2018)

• Decoupling Structure and Lexicon for Zero-Shot Semantic Parsing (Herzig & Berant, EMNLP 2018)

ACL

• Jonathan Herzig and Jonathan Berant. 2018. Decoupling Structure and Lexicon for Zero-Shot Semantic Parsing. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1619–1629, Brussels, Belgium. Association for Computational Linguistics.