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        <title>Toward Deconfounding the Effect of Entity Demographics for Question Answering Accuracy</title>
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        <namePart type="given">Maharshi</namePart>
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            <title>Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing</title>
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            <namePart type="given">Marie-Francine</namePart>
            <namePart type="family">Moens</namePart>
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        <name type="personal">
            <namePart type="given">Xuanjing</namePart>
            <namePart type="family">Huang</namePart>
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        <name type="personal">
            <namePart type="given">Lucia</namePart>
            <namePart type="family">Specia</namePart>
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    <abstract>The goal of question answering (QA) is to answer _any_ question. However, major QA datasets have skewed distributions over gender, profession, and nationality. Despite that skew, an analysis of model accuracy reveals little evidence that accuracy is lower for people based on gender or nationality; instead, there is more variation on professions (question topic) and question ambiguity. But QA‘s lack of representation could itself hide evidence of bias, necessitating QA datasets that better represent global diversity.</abstract>
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    <identifier type="doi">10.18653/v1/2021.emnlp-main.444</identifier>
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%0 Conference Proceedings
%T Toward Deconfounding the Effect of Entity Demographics for Question Answering Accuracy
%A Gor, Maharshi
%A Webster, Kellie
%A Boyd-Graber, Jordan
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F gor-etal-2021-toward
%X The goal of question answering (QA) is to answer _any_ question. However, major QA datasets have skewed distributions over gender, profession, and nationality. Despite that skew, an analysis of model accuracy reveals little evidence that accuracy is lower for people based on gender or nationality; instead, there is more variation on professions (question topic) and question ambiguity. But QA‘s lack of representation could itself hide evidence of bias, necessitating QA datasets that better represent global diversity.
%R 10.18653/v1/2021.emnlp-main.444
%U https://aclanthology.org/2021.emnlp-main.444/
%U https://doi.org/10.18653/v1/2021.emnlp-main.444
%P 5457-5473

Markdown (Informal)

[Toward Deconfounding the Effect of Entity Demographics for Question Answering Accuracy](https://aclanthology.org/2021.emnlp-main.444/) (Gor et al., EMNLP 2021)

• Toward Deconfounding the Effect of Entity Demographics for Question Answering Accuracy (Gor et al., EMNLP 2021)

ACL

• Maharshi Gor, Kellie Webster, and Jordan Boyd-Graber. 2021. Toward Deconfounding the Effect of Entity Demographics for Question Answering Accuracy. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 5457–5473, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.