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        <title>MSnet: A BERT-based Network for Gendered Pronoun Resolution</title>
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    <name type="personal">
        <namePart type="given">Zili</namePart>
        <namePart type="family">Wang</namePart>
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            <title>Proceedings of the First Workshop on Gender Bias in Natural Language Processing</title>
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        <name type="personal">
            <namePart type="given">Marta</namePart>
            <namePart type="given">R</namePart>
            <namePart type="family">Costa-jussà</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Christian</namePart>
            <namePart type="family">Hardmeier</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Will</namePart>
            <namePart type="family">Radford</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Kellie</namePart>
            <namePart type="family">Webster</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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    <abstract>The pre-trained BERT model achieves a remarkable state of the art across a wide range of tasks in natural language processing. For solving the gender bias in gendered pronoun resolution task, I propose a novel neural network model based on the pre-trained BERT. This model is a type of mention score classifier and uses an attention mechanism with no parameters to compute the contextual representation of entity span, and a vector to represent the triple-wise semantic similarity among the pronoun and the entities. In stage 1 of the gendered pronoun resolution task, a variant of this model, trained in the fine-tuning approach, reduced the multi-class logarithmic loss to 0.3033 in the 5-fold cross-validation of training set and 0.2795 in testing set. Besides, this variant won the 2nd place with a score at 0.17289 in stage 2 of the task. The code in this paper is available at: https://github.com/ziliwang/MSnet-for-Gendered-Pronoun-Resolution</abstract>
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    <identifier type="doi">10.18653/v1/W19-3813</identifier>
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        <date>2019-08</date>
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%0 Conference Proceedings
%T MSnet: A BERT-based Network for Gendered Pronoun Resolution
%A Wang, Zili
%Y Costa-jussà, Marta R.
%Y Hardmeier, Christian
%Y Radford, Will
%Y Webster, Kellie
%S Proceedings of the First Workshop on Gender Bias in Natural Language Processing
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F wang-2019-msnet
%X The pre-trained BERT model achieves a remarkable state of the art across a wide range of tasks in natural language processing. For solving the gender bias in gendered pronoun resolution task, I propose a novel neural network model based on the pre-trained BERT. This model is a type of mention score classifier and uses an attention mechanism with no parameters to compute the contextual representation of entity span, and a vector to represent the triple-wise semantic similarity among the pronoun and the entities. In stage 1 of the gendered pronoun resolution task, a variant of this model, trained in the fine-tuning approach, reduced the multi-class logarithmic loss to 0.3033 in the 5-fold cross-validation of training set and 0.2795 in testing set. Besides, this variant won the 2nd place with a score at 0.17289 in stage 2 of the task. The code in this paper is available at: https://github.com/ziliwang/MSnet-for-Gendered-Pronoun-Resolution
%R 10.18653/v1/W19-3813
%U https://aclanthology.org/W19-3813
%U https://doi.org/10.18653/v1/W19-3813
%P 89-95

Markdown (Informal)

[MSnet: A BERT-based Network for Gendered Pronoun Resolution](https://aclanthology.org/W19-3813) (Wang, GeBNLP 2019)

• MSnet: A BERT-based Network for Gendered Pronoun Resolution (Wang, GeBNLP 2019)

ACL

• Zili Wang. 2019. MSnet: A BERT-based Network for Gendered Pronoun Resolution. In Proceedings of the First Workshop on Gender Bias in Natural Language Processing, pages 89–95, Florence, Italy. Association for Computational Linguistics.