  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.
