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        <title>Label-Agnostic Sequence Labeling by Copying Nearest Neighbors</title>
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    <name type="personal">
        <namePart type="given">Karl</namePart>
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        <name type="personal">
            <namePart type="given">Anna</namePart>
            <namePart type="family">Korhonen</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">Traum</namePart>
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        <name type="personal">
            <namePart type="given">Lluís</namePart>
            <namePart type="family">Màrquez</namePart>
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    <abstract>Retrieve-and-edit based approaches to structured prediction, where structures associated with retrieved neighbors are edited to form new structures, have recently attracted increased interest. However, much recent work merely conditions on retrieved structures (e.g., in a sequence-to-sequence framework), rather than explicitly manipulating them. We show we can perform accurate sequence labeling by explicitly (and only) copying labels from retrieved neighbors. Moreover, because this copying is label-agnostic, we can achieve impressive performance in zero-shot sequence-labeling tasks. We additionally consider a dynamic programming approach to sequence labeling in the presence of retrieved neighbors, which allows for controlling the number of distinct (copied) segments used to form a prediction, and leads to both more interpretable and accurate predictions.</abstract>
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    <identifier type="doi">10.18653/v1/P19-1533</identifier>
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%0 Conference Proceedings
%T Label-Agnostic Sequence Labeling by Copying Nearest Neighbors
%A Wiseman, Sam
%A Stratos, Karl
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F wiseman-stratos-2019-label
%X Retrieve-and-edit based approaches to structured prediction, where structures associated with retrieved neighbors are edited to form new structures, have recently attracted increased interest. However, much recent work merely conditions on retrieved structures (e.g., in a sequence-to-sequence framework), rather than explicitly manipulating them. We show we can perform accurate sequence labeling by explicitly (and only) copying labels from retrieved neighbors. Moreover, because this copying is label-agnostic, we can achieve impressive performance in zero-shot sequence-labeling tasks. We additionally consider a dynamic programming approach to sequence labeling in the presence of retrieved neighbors, which allows for controlling the number of distinct (copied) segments used to form a prediction, and leads to both more interpretable and accurate predictions.
%R 10.18653/v1/P19-1533
%U https://aclanthology.org/P19-1533
%U https://doi.org/10.18653/v1/P19-1533
%P 5363-5369

Markdown (Informal)

[Label-Agnostic Sequence Labeling by Copying Nearest Neighbors](https://aclanthology.org/P19-1533) (Wiseman & Stratos, ACL 2019)

• Label-Agnostic Sequence Labeling by Copying Nearest Neighbors (Wiseman & Stratos, ACL 2019)

ACL

• Sam Wiseman and Karl Stratos. 2019. Label-Agnostic Sequence Labeling by Copying Nearest Neighbors. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 5363–5369, Florence, Italy. Association for Computational Linguistics.