Joint Dialogue Topic Segmentation and Categorization: A Case Study on Clinical Spoken Conversations

Zhengyuan Liu, Siti Umairah Md Salleh, Hong Choon Oh, Pavitra Krishnaswamy, Nancy Chen


Abstract
Utilizing natural language processing techniques in clinical conversations is effective to improve the efficiency of health management workflows for medical staff and patients. Dialogue segmentation and topic categorization are two fundamental steps for processing verbose spoken conversations and highlighting informative spans for downstream tasks. However, in practical use cases, due to the variety of segmentation granularity and topic definition, and the lack of diverse annotated corpora, no generic models are readily applicable for domain-specific applications. In this work, we introduce and adopt a joint model for dialogue segmentation and topic categorization, and conduct a case study on healthcare follow-up calls for diabetes management; we provide insights from both data and model perspectives toward performance and robustness.
Anthology ID:
2023.emnlp-industry.19
Volume:
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track
Month:
December
Year:
2023
Address:
Singapore
Editors:
Mingxuan Wang, Imed Zitouni
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
185–193
Language:
URL:
https://aclanthology.org/2023.emnlp-industry.19
DOI:
10.18653/v1/2023.emnlp-industry.19
Bibkey:
Cite (ACL):
Zhengyuan Liu, Siti Umairah Md Salleh, Hong Choon Oh, Pavitra Krishnaswamy, and Nancy Chen. 2023. Joint Dialogue Topic Segmentation and Categorization: A Case Study on Clinical Spoken Conversations. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 185–193, Singapore. Association for Computational Linguistics.
Cite (Informal):
Joint Dialogue Topic Segmentation and Categorization: A Case Study on Clinical Spoken Conversations (Liu et al., EMNLP 2023)
Copy Citation:
PDF:
https://aclanthology.org/2023.emnlp-industry.19.pdf
Video:
 https://aclanthology.org/2023.emnlp-industry.19.mp4