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    <titleInfo>
        <title>Identifying Domain Adjacent Instances for Semantic Parsers</title>
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    <name type="personal">
        <namePart type="given">James</namePart>
        <namePart type="family">Ferguson</namePart>
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    <name type="personal">
        <namePart type="given">Janara</namePart>
        <namePart type="family">Christensen</namePart>
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    <name type="personal">
        <namePart type="given">Edward</namePart>
        <namePart type="family">Li</namePart>
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    <name type="personal">
        <namePart type="given">Edgar</namePart>
        <namePart type="family">Gonzàlez</namePart>
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        <dateIssued>2018-oct-nov</dateIssued>
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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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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">David</namePart>
            <namePart type="family">Chiang</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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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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            <publisher>Association for Computational Linguistics</publisher>
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    <abstract>When the semantics of a sentence are not representable in a semantic parser’s output schema, parsing will inevitably fail. Detection of these instances is commonly treated as an out-of-domain classification problem. However, there is also a more subtle scenario in which the test data is drawn from the same domain. In addition to formalizing this problem of domain-adjacency, we present a comparison of various baselines that could be used to solve it. We also propose a new simple sentence representation that emphasizes words which are unexpected. This approach improves the performance of a downstream semantic parser run on in-domain and domain-adjacent instances.</abstract>
    <identifier type="citekey">ferguson-etal-2018-identifying</identifier>
    <identifier type="doi">10.18653/v1/D18-1539</identifier>
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    <part>
        <date>2018-oct-nov</date>
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            <start>4964</start>
            <end>4969</end>
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%0 Conference Proceedings
%T Identifying Domain Adjacent Instances for Semantic Parsers
%A Ferguson, James
%A Christensen, Janara
%A Li, Edward
%A Gonzàlez, Edgar
%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 ferguson-etal-2018-identifying
%X When the semantics of a sentence are not representable in a semantic parser’s output schema, parsing will inevitably fail. Detection of these instances is commonly treated as an out-of-domain classification problem. However, there is also a more subtle scenario in which the test data is drawn from the same domain. In addition to formalizing this problem of domain-adjacency, we present a comparison of various baselines that could be used to solve it. We also propose a new simple sentence representation that emphasizes words which are unexpected. This approach improves the performance of a downstream semantic parser run on in-domain and domain-adjacent instances.
%R 10.18653/v1/D18-1539
%U https://aclanthology.org/D18-1539
%U https://doi.org/10.18653/v1/D18-1539
%P 4964-4969

Markdown (Informal)

[Identifying Domain Adjacent Instances for Semantic Parsers](https://aclanthology.org/D18-1539) (Ferguson et al., EMNLP 2018)

• Identifying Domain Adjacent Instances for Semantic Parsers (Ferguson et al., EMNLP 2018)

ACL

• James Ferguson, Janara Christensen, Edward Li, and Edgar Gonzàlez. 2018. Identifying Domain Adjacent Instances for Semantic Parsers. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 4964–4969, Brussels, Belgium. Association for Computational Linguistics.