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        <title>A Co-Attentive Cross-Lingual Neural Model for Dialogue Breakdown Detection</title>
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        <namePart type="given">Qian</namePart>
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            <title>Proceedings of the 28th International Conference on Computational Linguistics</title>
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            <namePart type="given">Chengqing</namePart>
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    <abstract>Ensuring smooth communication is essential in a chat-oriented dialogue system, so that a user can obtain meaningful responses through interactions with the system. Most prior work on dialogue research does not focus on preventing dialogue breakdown. One of the major challenges is that a dialogue system may generate an undesired utterance leading to a dialogue breakdown, which degrades the overall interaction quality. Hence, it is crucial for a machine to detect dialogue breakdowns in an ongoing conversation. In this paper, we propose a novel dialogue breakdown detection model that jointly incorporates a pretrained cross-lingual language model and a co-attention network. Our proposed model leverages effective word embeddings trained on one hundred different languages to generate contextualized representations. Co-attention aims to capture the interaction between the latest utterance and the conversation history, and thereby determines whether the latest utterance causes a dialogue breakdown. Experimental results show that our proposed model outperforms all previous approaches on all evaluation metrics in both the Japanese and English tracks in Dialogue Breakdown Detection Challenge 4 (DBDC4 at IWSDS2019).</abstract>
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    <identifier type="doi">10.18653/v1/2020.coling-main.371</identifier>
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%0 Conference Proceedings
%T A Co-Attentive Cross-Lingual Neural Model for Dialogue Breakdown Detection
%A Lin, Qian
%A Kundu, Souvik
%A Ng, Hwee Tou
%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 lin-etal-2020-co
%X Ensuring smooth communication is essential in a chat-oriented dialogue system, so that a user can obtain meaningful responses through interactions with the system. Most prior work on dialogue research does not focus on preventing dialogue breakdown. One of the major challenges is that a dialogue system may generate an undesired utterance leading to a dialogue breakdown, which degrades the overall interaction quality. Hence, it is crucial for a machine to detect dialogue breakdowns in an ongoing conversation. In this paper, we propose a novel dialogue breakdown detection model that jointly incorporates a pretrained cross-lingual language model and a co-attention network. Our proposed model leverages effective word embeddings trained on one hundred different languages to generate contextualized representations. Co-attention aims to capture the interaction between the latest utterance and the conversation history, and thereby determines whether the latest utterance causes a dialogue breakdown. Experimental results show that our proposed model outperforms all previous approaches on all evaluation metrics in both the Japanese and English tracks in Dialogue Breakdown Detection Challenge 4 (DBDC4 at IWSDS2019).
%R 10.18653/v1/2020.coling-main.371
%U https://aclanthology.org/2020.coling-main.371
%U https://doi.org/10.18653/v1/2020.coling-main.371
%P 4201-4210

Markdown (Informal)

[A Co-Attentive Cross-Lingual Neural Model for Dialogue Breakdown Detection](https://aclanthology.org/2020.coling-main.371) (Lin et al., COLING 2020)

• A Co-Attentive Cross-Lingual Neural Model for Dialogue Breakdown Detection (Lin et al., COLING 2020)

ACL

• Qian Lin, Souvik Kundu, and Hwee Tou Ng. 2020. A Co-Attentive Cross-Lingual Neural Model for Dialogue Breakdown Detection. In Proceedings of the 28th International Conference on Computational Linguistics, pages 4201–4210, Barcelona, Spain (Online). International Committee on Computational Linguistics.