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        <title>Improving Generalization in Coreference Resolution via Adversarial Training</title>
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    <name type="personal">
        <namePart type="given">Sanjay</namePart>
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            <title>Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM 2019)</title>
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        <name type="personal">
            <namePart type="given">Rada</namePart>
            <namePart type="family">Mihalcea</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Ekaterina</namePart>
            <namePart type="family">Shutova</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Lun-Wei</namePart>
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            <namePart type="given">Kilian</namePart>
            <namePart type="family">Evang</namePart>
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        <name type="personal">
            <namePart type="given">Soujanya</namePart>
            <namePart type="family">Poria</namePart>
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    <abstract>In order for coreference resolution systems to be useful in practice, they must be able to generalize to new text. In this work, we demonstrate that the performance of the state-of-the-art system decreases when the names of PER and GPE named entities in the CoNLL dataset are changed to names that do not occur in the training set. We use the technique of adversarial gradient-based training to retrain the state-of-the-art system and demonstrate that the retrained system achieves higher performance on the CoNLL dataset (both with and without the change of named entities) and the GAP dataset.</abstract>
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%0 Conference Proceedings
%T Improving Generalization in Coreference Resolution via Adversarial Training
%A Subramanian, Sanjay
%A Roth, Dan
%Y Mihalcea, Rada
%Y Shutova, Ekaterina
%Y Ku, Lun-Wei
%Y Evang, Kilian
%Y Poria, Soujanya
%S Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM 2019)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F subramanian-roth-2019-improving
%X In order for coreference resolution systems to be useful in practice, they must be able to generalize to new text. In this work, we demonstrate that the performance of the state-of-the-art system decreases when the names of PER and GPE named entities in the CoNLL dataset are changed to names that do not occur in the training set. We use the technique of adversarial gradient-based training to retrain the state-of-the-art system and demonstrate that the retrained system achieves higher performance on the CoNLL dataset (both with and without the change of named entities) and the GAP dataset.
%R 10.18653/v1/S19-1021
%U https://aclanthology.org/S19-1021
%U https://doi.org/10.18653/v1/S19-1021
%P 192-197

Markdown (Informal)

[Improving Generalization in Coreference Resolution via Adversarial Training](https://aclanthology.org/S19-1021) (Subramanian & Roth, *SEM 2019)

• Improving Generalization in Coreference Resolution via Adversarial Training (Subramanian & Roth, *SEM 2019)

ACL

• Sanjay Subramanian and Dan Roth. 2019. Improving Generalization in Coreference Resolution via Adversarial Training. In Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM 2019), pages 192–197, Minneapolis, Minnesota. Association for Computational Linguistics.