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        <title>Dialogue Act-Aided Backchannel Prediction Using Multi-Task Learning</title>
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        <namePart type="given">Wencke</namePart>
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        <namePart type="given">Yo-Han</namePart>
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        <namePart type="given">Kong</namePart>
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    <abstract>Produced in the form of small injections such as “Yeah!” or “Uh-Huh” by listeners in a conversation, supportive verbal feedback (i.e., backchanneling) is essential for natural dialogue. Highlighting its tight relation to speaker intent and utterance type, we propose a multi-task learning approach that learns textual representations for the task of backchannel prediction in tandem with dialogue act classification. We demonstrate the effectiveness of our approach by improving the prediction of specific backchannels like “Yeah” or “Really?” by up to 2.0% in F1. Additionally, whereas previous models relied on well-established methods to extract audio features, we further pre-train the audio encoder in a self-supervised fashion using voice activity projection. This leads to additional gains of 1.4% in weighted F1.</abstract>
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    <identifier type="doi">10.18653/v1/2023.findings-emnlp.1006</identifier>
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%0 Conference Proceedings
%T Dialogue Act-Aided Backchannel Prediction Using Multi-Task Learning
%A Liermann, Wencke
%A Park, Yo-Han
%A Choi, Yong-Seok
%A Lee, Kong
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F liermann-etal-2023-dialogue
%X Produced in the form of small injections such as “Yeah!” or “Uh-Huh” by listeners in a conversation, supportive verbal feedback (i.e., backchanneling) is essential for natural dialogue. Highlighting its tight relation to speaker intent and utterance type, we propose a multi-task learning approach that learns textual representations for the task of backchannel prediction in tandem with dialogue act classification. We demonstrate the effectiveness of our approach by improving the prediction of specific backchannels like “Yeah” or “Really?” by up to 2.0% in F1. Additionally, whereas previous models relied on well-established methods to extract audio features, we further pre-train the audio encoder in a self-supervised fashion using voice activity projection. This leads to additional gains of 1.4% in weighted F1.
%R 10.18653/v1/2023.findings-emnlp.1006
%U https://aclanthology.org/2023.findings-emnlp.1006
%U https://doi.org/10.18653/v1/2023.findings-emnlp.1006
%P 15073-15079

Markdown (Informal)

[Dialogue Act-Aided Backchannel Prediction Using Multi-Task Learning](https://aclanthology.org/2023.findings-emnlp.1006) (Liermann et al., Findings 2023)

• Dialogue Act-Aided Backchannel Prediction Using Multi-Task Learning (Liermann et al., Findings 2023)

ACL

• Wencke Liermann, Yo-Han Park, Yong-Seok Choi, and Kong Lee. 2023. Dialogue Act-Aided Backchannel Prediction Using Multi-Task Learning. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 15073–15079, Singapore. Association for Computational Linguistics.