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        <title>Adapting Coreference Resolution to Twitter Conversations</title>
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    <name type="personal">
        <namePart type="given">Berfin</namePart>
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        <namePart type="given">Veronika</namePart>
        <namePart type="family">Solopova</namePart>
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    <name type="personal">
        <namePart type="given">Annalena</namePart>
        <namePart type="family">Kohnert</namePart>
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    <name type="personal">
        <namePart type="given">Manfred</namePart>
        <namePart type="family">Stede</namePart>
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            <namePart type="given">Trevor</namePart>
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            <namePart type="given">Yulan</namePart>
            <namePart type="family">He</namePart>
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            <namePart type="given">Yang</namePart>
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    <abstract>The performance of standard coreference resolution is known to drop significantly on Twitter texts. We improve the performance of the (Lee et al., 2018) system, which is originally trained on OntoNotes, by retraining on manually-annotated Twitter conversation data. Further experiments by combining different portions of OntoNotes with Twitter data show that selecting text genres for the training data can beat the mere maximization of training data amount. In addition, we inspect several phenomena such as the role of deictic pronouns in conversational data, and present additional results for variant settings. Our best configuration improves the performance of the”out of the box” system by 21.6%.</abstract>
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    <identifier type="doi">10.18653/v1/2020.findings-emnlp.222</identifier>
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        <url>https://aclanthology.org/2020.findings-emnlp.222</url>
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        <date>2020-11</date>
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%0 Conference Proceedings
%T Adapting Coreference Resolution to Twitter Conversations
%A Aktaş, Berfin
%A Solopova, Veronika
%A Kohnert, Annalena
%A Stede, Manfred
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F aktas-etal-2020-adapting
%X The performance of standard coreference resolution is known to drop significantly on Twitter texts. We improve the performance of the (Lee et al., 2018) system, which is originally trained on OntoNotes, by retraining on manually-annotated Twitter conversation data. Further experiments by combining different portions of OntoNotes with Twitter data show that selecting text genres for the training data can beat the mere maximization of training data amount. In addition, we inspect several phenomena such as the role of deictic pronouns in conversational data, and present additional results for variant settings. Our best configuration improves the performance of the”out of the box” system by 21.6%.
%R 10.18653/v1/2020.findings-emnlp.222
%U https://aclanthology.org/2020.findings-emnlp.222
%U https://doi.org/10.18653/v1/2020.findings-emnlp.222
%P 2454-2460

Markdown (Informal)

[Adapting Coreference Resolution to Twitter Conversations](https://aclanthology.org/2020.findings-emnlp.222) (Aktaş et al., Findings 2020)

• Adapting Coreference Resolution to Twitter Conversations (Aktaş et al., Findings 2020)

ACL

• Berfin Aktaş, Veronika Solopova, Annalena Kohnert, and Manfred Stede. 2020. Adapting Coreference Resolution to Twitter Conversations. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 2454–2460, Online. Association for Computational Linguistics.