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        <title>Reading Turn by Turn: Hierarchical Attention Architecture for Spoken Dialogue Comprehension</title>
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    <abstract>Comprehending multi-turn spoken conversations is an emerging research area, presenting challenges different from reading comprehension of passages due to the interactive nature of information exchange from at least two speakers. Unlike passages, where sentences are often the default semantic modeling unit, in multi-turn conversations, a turn is a topically coherent unit embodied with immediately relevant context, making it a linguistically intuitive segment for computationally modeling verbal interactions. Therefore, in this work, we propose a hierarchical attention neural network architecture, combining turn-level and word-level attention mechanisms, to improve spoken dialogue comprehension performance. Experiments are conducted on a multi-turn conversation dataset, where nurses inquire and discuss symptom information with patients. We empirically show that the proposed approach outperforms standard attention baselines, achieves more efficient learning outcomes, and is more robust to lengthy and out-of-distribution test samples.</abstract>
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    <identifier type="doi">10.18653/v1/P19-1543</identifier>
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%0 Conference Proceedings
%T Reading Turn by Turn: Hierarchical Attention Architecture for Spoken Dialogue Comprehension
%A Liu, Zhengyuan
%A Chen, Nancy
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F liu-chen-2019-reading
%X Comprehending multi-turn spoken conversations is an emerging research area, presenting challenges different from reading comprehension of passages due to the interactive nature of information exchange from at least two speakers. Unlike passages, where sentences are often the default semantic modeling unit, in multi-turn conversations, a turn is a topically coherent unit embodied with immediately relevant context, making it a linguistically intuitive segment for computationally modeling verbal interactions. Therefore, in this work, we propose a hierarchical attention neural network architecture, combining turn-level and word-level attention mechanisms, to improve spoken dialogue comprehension performance. Experiments are conducted on a multi-turn conversation dataset, where nurses inquire and discuss symptom information with patients. We empirically show that the proposed approach outperforms standard attention baselines, achieves more efficient learning outcomes, and is more robust to lengthy and out-of-distribution test samples.
%R 10.18653/v1/P19-1543
%U https://aclanthology.org/P19-1543
%U https://doi.org/10.18653/v1/P19-1543
%P 5460-5466

Markdown (Informal)

[Reading Turn by Turn: Hierarchical Attention Architecture for Spoken Dialogue Comprehension](https://aclanthology.org/P19-1543) (Liu & Chen, ACL 2019)

• Reading Turn by Turn: Hierarchical Attention Architecture for Spoken Dialogue Comprehension (Liu & Chen, ACL 2019)

ACL

• Zhengyuan Liu and Nancy Chen. 2019. Reading Turn by Turn: Hierarchical Attention Architecture for Spoken Dialogue Comprehension. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 5460–5466, Florence, Italy. Association for Computational Linguistics.