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        <title>Multi-turn Response Selection using Dialogue Dependency Relations</title>
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        <namePart type="given">Yizhu</namePart>
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        <namePart type="given">Siyu</namePart>
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        <namePart type="given">Haifeng</namePart>
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            <title>Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)</title>
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            <namePart type="given">Yang</namePart>
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    <abstract>Multi-turn response selection is a task designed for developing dialogue agents. The performance on this task has a remarkable improvement with pre-trained language models. However, these models simply concatenate the turns in dialogue history as the input and largely ignore the dependencies between the turns. In this paper, we propose a dialogue extraction algorithm to transform a dialogue history into threads based on their dependency relations. Each thread can be regarded as a self-contained sub-dialogue. We also propose Thread-Encoder model to encode threads and candidates into compact representations by pre-trained Transformers and finally get the matching score through an attention layer. The experiments show that dependency relations are helpful for dialogue context understanding, and our model outperforms the state-of-the-art baselines on both DSTC7 and DSTC8*, with competitive results on UbuntuV2.</abstract>
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        <date>2020-11</date>
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            <start>1911</start>
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%0 Conference Proceedings
%T Multi-turn Response Selection using Dialogue Dependency Relations
%A Jia, Qi
%A Liu, Yizhu
%A Ren, Siyu
%A Zhu, Kenny
%A Tang, Haifeng
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F jia-etal-2020-multi
%X Multi-turn response selection is a task designed for developing dialogue agents. The performance on this task has a remarkable improvement with pre-trained language models. However, these models simply concatenate the turns in dialogue history as the input and largely ignore the dependencies between the turns. In this paper, we propose a dialogue extraction algorithm to transform a dialogue history into threads based on their dependency relations. Each thread can be regarded as a self-contained sub-dialogue. We also propose Thread-Encoder model to encode threads and candidates into compact representations by pre-trained Transformers and finally get the matching score through an attention layer. The experiments show that dependency relations are helpful for dialogue context understanding, and our model outperforms the state-of-the-art baselines on both DSTC7 and DSTC8*, with competitive results on UbuntuV2.
%R 10.18653/v1/2020.emnlp-main.150
%U https://aclanthology.org/2020.emnlp-main.150
%U https://doi.org/10.18653/v1/2020.emnlp-main.150
%P 1911-1920

Markdown (Informal)

[Multi-turn Response Selection using Dialogue Dependency Relations](https://aclanthology.org/2020.emnlp-main.150) (Jia et al., EMNLP 2020)

• Multi-turn Response Selection using Dialogue Dependency Relations (Jia et al., EMNLP 2020)

ACL

• Qi Jia, Yizhu Liu, Siyu Ren, Kenny Zhu, and Haifeng Tang. 2020. Multi-turn Response Selection using Dialogue Dependency Relations. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1911–1920, Online. Association for Computational Linguistics.