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    <titleInfo>
        <title>Mind the GAP: A Balanced Corpus of Gendered Ambiguous Pronouns</title>
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    <name type="personal">
        <namePart type="given">Kellie</namePart>
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        <namePart type="given">Marta</namePart>
        <namePart type="family">Recasens</namePart>
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    <name type="personal">
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        <namePart type="given">Jason</namePart>
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    <abstract>Coreference resolution is an important task for natural language understanding, and the resolution of ambiguous pronouns a longstanding challenge. Nonetheless, existing corpora do not capture ambiguous pronouns in sufficient volume or diversity to accurately indicate the practical utility of models. Furthermore, we find gender bias in existing corpora and systems favoring masculine entities. To address this, we present and release GAP, a gender-balanced labeled corpus of 8,908 ambiguous pronoun–name pairs sampled to provide diverse coverage of challenges posed by real-world text. We explore a range of baselines that demonstrate the complexity of the challenge, the best achieving just 66.9% F1. We show that syntactic structure and continuous neural models provide promising, complementary cues for approaching the challenge.</abstract>
    <identifier type="citekey">webster-etal-2018-mind</identifier>
    <identifier type="doi">10.1162/tacl_a_00240</identifier>
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        <date>2018</date>
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%0 Journal Article
%T Mind the GAP: A Balanced Corpus of Gendered Ambiguous Pronouns
%A Webster, Kellie
%A Recasens, Marta
%A Axelrod, Vera
%A Baldridge, Jason
%J Transactions of the Association for Computational Linguistics
%D 2018
%V 6
%I MIT Press
%C Cambridge, MA
%F webster-etal-2018-mind
%X Coreference resolution is an important task for natural language understanding, and the resolution of ambiguous pronouns a longstanding challenge. Nonetheless, existing corpora do not capture ambiguous pronouns in sufficient volume or diversity to accurately indicate the practical utility of models. Furthermore, we find gender bias in existing corpora and systems favoring masculine entities. To address this, we present and release GAP, a gender-balanced labeled corpus of 8,908 ambiguous pronoun–name pairs sampled to provide diverse coverage of challenges posed by real-world text. We explore a range of baselines that demonstrate the complexity of the challenge, the best achieving just 66.9% F1. We show that syntactic structure and continuous neural models provide promising, complementary cues for approaching the challenge.
%R 10.1162/tacl_a_00240
%U https://aclanthology.org/Q18-1042
%U https://doi.org/10.1162/tacl_a_00240
%P 605-617

Markdown (Informal)

[Mind the GAP: A Balanced Corpus of Gendered Ambiguous Pronouns](https://aclanthology.org/Q18-1042) (Webster et al., TACL 2018)

• Mind the GAP: A Balanced Corpus of Gendered Ambiguous Pronouns (Webster et al., TACL 2018)

ACL

• Kellie Webster, Marta Recasens, Vera Axelrod, and Jason Baldridge. 2018. Mind the GAP: A Balanced Corpus of Gendered Ambiguous Pronouns. Transactions of the Association for Computational Linguistics, 6:605–617.