@inproceedings{liu-etal-2023-joint,
title = "Joint Dialogue Topic Segmentation and Categorization: A Case Study on Clinical Spoken Conversations",
author = "Liu, Zhengyuan and
Md Salleh, Siti Umairah and
Oh, Hong Choon and
Krishnaswamy, Pavitra and
Chen, Nancy",
editor = "Wang, Mingxuan and
Zitouni, Imed",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-industry.19",
doi = "10.18653/v1/2023.emnlp-industry.19",
pages = "185--193",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Joint Dialogue Topic Segmentation and Categorization: A Case Study on Clinical Spoken Conversations
%A Liu, Zhengyuan
%A Md Salleh, Siti Umairah
%A Oh, Hong Choon
%A Krishnaswamy, Pavitra
%A Chen, Nancy
%Y Wang, Mingxuan
%Y Zitouni, Imed
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F liu-etal-2023-joint
%X 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.
%R 10.18653/v1/2023.emnlp-industry.19
%U https://aclanthology.org/2023.emnlp-industry.19
%U https://doi.org/10.18653/v1/2023.emnlp-industry.19
%P 185-193
Markdown (Informal)
[Joint Dialogue Topic Segmentation and Categorization: A Case Study on Clinical Spoken Conversations](https://aclanthology.org/2023.emnlp-industry.19) (Liu et al., EMNLP 2023)
ACL