KNSE: A Knowledge-aware Natural Language Inference Framework for Dialogue Symptom Status Recognition
Abstract
Symptom diagnosis in medical conversations aims to correctly extract both symptom entities and their status from the doctor-patient dialogue. In this paper, we propose a novel framework called KNSE for symptom status recognition (SSR), where the SSR is formulated as a natural language inference (NLI) task. For each mentioned symptom in a dialogue window, we first generate knowledge about the symptom and hypothesis about status of the symptom, to form a (premise, knowledge, hypothesis) triplet. The BERT model is then used to encode the triplet, which is further processed by modules including utterance aggregation, self-attention, cross-attention, and GRU to predict the symptom status. Benefiting from the NLI formalization, the proposed framework can encode more informative prior knowledge to better localize and track symptom status, which can effectively improve the performance of symptom status recognition. Preliminary experiments on Chinese medical dialogue datasets show that KNSE outperforms previous competitive baselines and has advantages in cross-disease and cross-symptom scenarios.- Anthology ID:
- 2023.findings-acl.652
- Volume:
- Findings of the Association for Computational Linguistics: ACL 2023
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 10278–10286
- Language:
- URL:
- https://aclanthology.org/2023.findings-acl.652
- DOI:
- 10.18653/v1/2023.findings-acl.652
- Cite (ACL):
- Wei Chen, Shiqi Wei, Zhongyu Wei, and Xuanjing Huang. 2023. KNSE: A Knowledge-aware Natural Language Inference Framework for Dialogue Symptom Status Recognition. In Findings of the Association for Computational Linguistics: ACL 2023, pages 10278–10286, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- KNSE: A Knowledge-aware Natural Language Inference Framework for Dialogue Symptom Status Recognition (Chen et al., Findings 2023)
- PDF:
- https://preview.aclanthology.org/emnlp22-frontmatter/2023.findings-acl.652.pdf