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        <title>Do Neural Language Models Overcome Reporting Bias?</title>
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            <title>Proceedings of the 28th International Conference on Computational Linguistics</title>
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            <namePart type="given">Donia</namePart>
            <namePart type="family">Scott</namePart>
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        <name type="personal">
            <namePart type="given">Nuria</namePart>
            <namePart type="family">Bel</namePart>
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        <name type="personal">
            <namePart type="given">Chengqing</namePart>
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    <abstract>Mining commonsense knowledge from corpora suffers from reporting bias, over-representing the rare at the expense of the trivial (Gordon and Van Durme, 2013). We study to what extent pre-trained language models overcome this issue. We find that while their generalization capacity allows them to better estimate the plausibility of frequent but unspoken of actions, outcomes, and properties, they also tend to overestimate that of the very rare, amplifying the bias that already exists in their training corpus.</abstract>
    <identifier type="citekey">shwartz-choi-2020-neural</identifier>
    <identifier type="doi">10.18653/v1/2020.coling-main.605</identifier>
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%0 Conference Proceedings
%T Do Neural Language Models Overcome Reporting Bias?
%A Shwartz, Vered
%A Choi, Yejin
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F shwartz-choi-2020-neural
%X Mining commonsense knowledge from corpora suffers from reporting bias, over-representing the rare at the expense of the trivial (Gordon and Van Durme, 2013). We study to what extent pre-trained language models overcome this issue. We find that while their generalization capacity allows them to better estimate the plausibility of frequent but unspoken of actions, outcomes, and properties, they also tend to overestimate that of the very rare, amplifying the bias that already exists in their training corpus.
%R 10.18653/v1/2020.coling-main.605
%U https://aclanthology.org/2020.coling-main.605
%U https://doi.org/10.18653/v1/2020.coling-main.605
%P 6863-6870

Markdown (Informal)

[Do Neural Language Models Overcome Reporting Bias?](https://aclanthology.org/2020.coling-main.605) (Shwartz & Choi, COLING 2020)

• Do Neural Language Models Overcome Reporting Bias? (Shwartz & Choi, COLING 2020)

ACL

• Vered Shwartz and Yejin Choi. 2020. Do Neural Language Models Overcome Reporting Bias?. In Proceedings of the 28th International Conference on Computational Linguistics, pages 6863–6870, Barcelona, Spain (Online). International Committee on Computational Linguistics.