Reading Turn by Turn: Hierarchical Attention Architecture for Spoken Dialogue Comprehension

Zhengyuan Liu, Nancy Chen


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.
Anthology ID:
P19-1543
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5460–5466
Language:
URL:
https://aclanthology.org/P19-1543
DOI:
10.18653/v1/P19-1543
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/P19-1543.pdf
Data
SQuAD