Work

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

Year: 2023

Type: conference-paper

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.

Source: Conference on Empirical Methods in Natural Language Processing (EMNLP)

DOI: 10.18653/v1/2023.emnlp-industry.19

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

Institutions: Institute for Infocomm Research, Changi General Hospital

Language: English

Topic: Speech and dialogue systems

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: hybrid

Funders: Agency for Science, Technology and Research, Changi General Hospital, Institute for Infocomm Research