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        <title>More Diverse Dialogue Datasets via Diversity-Informed Data Collection</title>
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        <namePart type="given">Katherine</namePart>
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        <namePart type="given">Hui</namePart>
        <namePart type="family">Yang</namePart>
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        <namePart type="given">Marti</namePart>
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            <title>Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics</title>
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            <namePart type="given">Dan</namePart>
            <namePart type="family">Jurafsky</namePart>
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    <abstract>Automated generation of conversational dialogue using modern neural architectures has made notable advances. However, these models are known to have a drawback of often producing uninteresting, predictable responses; this is known as the diversity problem. We introduce a new strategy to address this problem, called Diversity-Informed Data Collection. Unlike prior approaches, which modify model architectures to solve the problem, this method uses dynamically computed corpus-level statistics to determine which conversational participants to collect data from. Diversity-Informed Data Collection produces significantly more diverse data than baseline data collection methods, and better results on two downstream tasks: emotion classification and dialogue generation. This method is generalizable and can be used with other corpus-level metrics.</abstract>
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        <date>2020-07</date>
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%0 Conference Proceedings
%T More Diverse Dialogue Datasets via Diversity-Informed Data Collection
%A Stasaski, Katherine
%A Yang, Grace Hui
%A Hearst, Marti A.
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F stasaski-etal-2020-diverse
%X Automated generation of conversational dialogue using modern neural architectures has made notable advances. However, these models are known to have a drawback of often producing uninteresting, predictable responses; this is known as the diversity problem. We introduce a new strategy to address this problem, called Diversity-Informed Data Collection. Unlike prior approaches, which modify model architectures to solve the problem, this method uses dynamically computed corpus-level statistics to determine which conversational participants to collect data from. Diversity-Informed Data Collection produces significantly more diverse data than baseline data collection methods, and better results on two downstream tasks: emotion classification and dialogue generation. This method is generalizable and can be used with other corpus-level metrics.
%R 10.18653/v1/2020.acl-main.446
%U https://aclanthology.org/2020.acl-main.446/
%U https://doi.org/10.18653/v1/2020.acl-main.446
%P 4958-4968

Markdown (Informal)

[More Diverse Dialogue Datasets via Diversity-Informed Data Collection](https://aclanthology.org/2020.acl-main.446/) (Stasaski et al., ACL 2020)

• More Diverse Dialogue Datasets via Diversity-Informed Data Collection (Stasaski et al., ACL 2020)

ACL

• Katherine Stasaski, Grace Hui Yang, and Marti A. Hearst. 2020. More Diverse Dialogue Datasets via Diversity-Informed Data Collection. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 4958–4968, Online. Association for Computational Linguistics.