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
- 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)
- PDF:
- https://preview.aclanthology.org/naacl-24-ws-corrections/2023.emnlp-industry.19.pdf