A Speaker-Aware Co-Attention Framework for Medical Dialogue Information Extraction

Yuan Xia, Zhenhui Shi, Jingbo Zhou, Jiayu Xu, Chao Lu, Yehui Yang, Lei Wang, Haifeng Huang, Xia Zhang, Junwei Liu


Abstract
With the development of medical digitization, the extraction and structuring of Electronic Medical Records (EMRs) have become challenging but fundamental tasks. How to accurately and automatically extract structured information from medical dialogues is especially difficult because the information needs to be inferred from complex interactions between the doctor and the patient. To this end, in this paper, we propose a speaker-aware co-attention framework for medical dialogue information extraction. To better utilize the pre-trained language representation model to perceive the semantics of the utterance and the candidate item, we develop a speaker-aware dialogue encoder with multi-task learning, which considers the speaker’s identity into account. To deal with complex interactions between different utterances and the correlations between utterances and candidate items, we propose a co-attention fusion network to aggregate the utterance information. We evaluate our framework on the public medical dialogue extraction datasets to demonstrate the superiority of our method, which can outperform the state-of-the-art methods by a large margin. Codes will be publicly available upon acceptance.
Anthology ID:
2022.emnlp-main.315
Volume:
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Editors:
Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4777–4786
Language:
URL:
https://aclanthology.org/2022.emnlp-main.315
DOI:
10.18653/v1/2022.emnlp-main.315
Bibkey:
Cite (ACL):
Yuan Xia, Zhenhui Shi, Jingbo Zhou, Jiayu Xu, Chao Lu, Yehui Yang, Lei Wang, Haifeng Huang, Xia Zhang, and Junwei Liu. 2022. A Speaker-Aware Co-Attention Framework for Medical Dialogue Information Extraction. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 4777–4786, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
A Speaker-Aware Co-Attention Framework for Medical Dialogue Information Extraction (Xia et al., EMNLP 2022)
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PDF:
https://aclanthology.org/2022.emnlp-main.315.pdf