Existing medical text datasets usually take the form of question and answer pairs that support the task of natural language generation, but lacking the composite annotations of the medical terms. In this study, we publish a Vietnamese dataset of medical questions from patients with sentence-level and entity-level annotations for the Intent Classification and Named Entity Recognition tasks. The tag sets for two tasks are in medical domain and can facilitate the development of task-oriented healthcare chatbots with better comprehension of queries from patients. We train baseline models for the two tasks and propose a simple self-supervised training strategy with span-noise modelling that substantially improves the performance. Dataset and code will be published at https://github.com/tadeephuy/ViMQ
@article{arxiv.2304.14405,
title = {ViMQ: A Vietnamese Medical Question Dataset for Healthcare Dialogue System Development},
author = {Ta Duc Huy and Nguyen Anh Tu and Tran Hoang Vu and Nguyen Phuc Minh and Nguyen Phan and Trung H. Bui and Steven Q. H. Truong},
journal= {arXiv preprint arXiv:2304.14405},
year = {2024}
}