KNSE: A Knowledge-aware Natural Language Inference Framework for Dialogue Symptom Status Recognition
This work addresses the problem of accurately extracting symptom statuses from doctor-patient conversations for medical diagnosis, representing an incremental improvement in a domain-specific application.
The paper tackles symptom status recognition in medical dialogues by formulating it as a natural language inference task, and the proposed KNSE framework outperforms previous baselines in experiments on Chinese datasets, showing advantages in cross-disease and cross-symptom scenarios.
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.