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        <title>Practical Semantic Parsing for Spoken Language Understanding</title>
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    <name type="personal">
        <namePart type="given">Marco</namePart>
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        <namePart type="given">Rahul</namePart>
        <namePart type="family">Goel</namePart>
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    <name type="personal">
        <namePart type="given">Tagyoung</namePart>
        <namePart type="family">Chung</namePart>
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        <dateIssued>2019-06</dateIssued>
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            <title>Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers)</title>
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            <namePart type="given">Anastassia</namePart>
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            <namePart type="given">Michelle</namePart>
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            <namePart type="given">Rohit</namePart>
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    <abstract>Executable semantic parsing is the task of converting natural language utterances into logical forms that can be directly used as queries to get a response. We build a transfer learning framework for executable semantic parsing. We show that the framework is effective for Question Answering (Q&amp;A) as well as for Spoken Language Understanding (SLU). We further investigate the case where a parser on a new domain can be learned by exploiting data on other domains, either via multi-task learning between the target domain and an auxiliary domain or via pre-training on the auxiliary domain and fine-tuning on the target domain. With either flavor of transfer learning, we are able to improve performance on most domains; we experiment with public data sets such as Overnight and NLmaps as well as with commercial SLU data. The experiments carried out on data sets that are different in nature show how executable semantic parsing can unify different areas of NLP such as Q&amp;A and SLU.</abstract>
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    <identifier type="doi">10.18653/v1/N19-2003</identifier>
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        <date>2019-06</date>
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%0 Conference Proceedings
%T Practical Semantic Parsing for Spoken Language Understanding
%A Damonte, Marco
%A Goel, Rahul
%A Chung, Tagyoung
%Y Loukina, Anastassia
%Y Morales, Michelle
%Y Kumar, Rohit
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F damonte-etal-2019-practical
%X Executable semantic parsing is the task of converting natural language utterances into logical forms that can be directly used as queries to get a response. We build a transfer learning framework for executable semantic parsing. We show that the framework is effective for Question Answering (Q&A) as well as for Spoken Language Understanding (SLU). We further investigate the case where a parser on a new domain can be learned by exploiting data on other domains, either via multi-task learning between the target domain and an auxiliary domain or via pre-training on the auxiliary domain and fine-tuning on the target domain. With either flavor of transfer learning, we are able to improve performance on most domains; we experiment with public data sets such as Overnight and NLmaps as well as with commercial SLU data. The experiments carried out on data sets that are different in nature show how executable semantic parsing can unify different areas of NLP such as Q&A and SLU.
%R 10.18653/v1/N19-2003
%U https://aclanthology.org/N19-2003
%U https://doi.org/10.18653/v1/N19-2003
%P 16-23

Markdown (Informal)

[Practical Semantic Parsing for Spoken Language Understanding](https://aclanthology.org/N19-2003) (Damonte et al., NAACL 2019)

• Practical Semantic Parsing for Spoken Language Understanding (Damonte et al., NAACL 2019)

ACL

• Marco Damonte, Rahul Goel, and Tagyoung Chung. 2019. Practical Semantic Parsing for Spoken Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers), pages 16–23, Minneapolis, Minnesota. Association for Computational Linguistics.