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        <title>An Evaluation Dataset and Strategy for Building Robust Multi-turn Response Selection Model</title>
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    <name type="personal">
        <namePart type="given">Kijong</namePart>
        <namePart type="family">Han</namePart>
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    <name type="personal">
        <namePart type="given">Seojin</namePart>
        <namePart type="family">Lee</namePart>
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    <name type="personal">
        <namePart type="given">Dong-hun</namePart>
        <namePart type="family">Lee</namePart>
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            <title>Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing</title>
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        <name type="personal">
            <namePart type="given">Marie-Francine</namePart>
            <namePart type="family">Moens</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Xuanjing</namePart>
            <namePart type="family">Huang</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Lucia</namePart>
            <namePart type="family">Specia</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Scott</namePart>
            <namePart type="given">Wen-tau</namePart>
            <namePart type="family">Yih</namePart>
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            <publisher>Association for Computational Linguistics</publisher>
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                <placeTerm type="text">Online and Punta Cana, Dominican Republic</placeTerm>
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    <abstract>Multi-turn response selection models have recently shown comparable performance to humans in several benchmark datasets. However, in the real environment, these models often have weaknesses, such as making incorrect predictions based heavily on superficial patterns without a comprehensive understanding of the context. For example, these models often give a high score to the wrong response candidate containing several keywords related to the context but using the inconsistent tense. In this study, we analyze the weaknesses of the open-domain Korean Multi-turn response selection models and publish an adversarial dataset to evaluate these weaknesses. We also suggest a strategy to build a robust model in this adversarial environment.</abstract>
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    <identifier type="doi">10.18653/v1/2021.emnlp-main.180</identifier>
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        <url>https://aclanthology.org/2021.emnlp-main.180/</url>
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    <part>
        <date>2021-11</date>
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            <start>2338</start>
            <end>2344</end>
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%0 Conference Proceedings
%T An Evaluation Dataset and Strategy for Building Robust Multi-turn Response Selection Model
%A Han, Kijong
%A Lee, Seojin
%A Lee, Dong-hun
%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 han-etal-2021-evaluation
%X Multi-turn response selection models have recently shown comparable performance to humans in several benchmark datasets. However, in the real environment, these models often have weaknesses, such as making incorrect predictions based heavily on superficial patterns without a comprehensive understanding of the context. For example, these models often give a high score to the wrong response candidate containing several keywords related to the context but using the inconsistent tense. In this study, we analyze the weaknesses of the open-domain Korean Multi-turn response selection models and publish an adversarial dataset to evaluate these weaknesses. We also suggest a strategy to build a robust model in this adversarial environment.
%R 10.18653/v1/2021.emnlp-main.180
%U https://aclanthology.org/2021.emnlp-main.180/
%U https://doi.org/10.18653/v1/2021.emnlp-main.180
%P 2338-2344

Markdown (Informal)

[An Evaluation Dataset and Strategy for Building Robust Multi-turn Response Selection Model](https://aclanthology.org/2021.emnlp-main.180/) (Han et al., EMNLP 2021)

• An Evaluation Dataset and Strategy for Building Robust Multi-turn Response Selection Model (Han et al., EMNLP 2021)

ACL

• Kijong Han, Seojin Lee, and Dong-hun Lee. 2021. An Evaluation Dataset and Strategy for Building Robust Multi-turn Response Selection Model. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 2338–2344, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.