# KNSE: A Knowledge-aware Natural Language Inference Framework for Dialogue Symptom Status Recognition Source: [KNSE: A Knowledge-aware Natural Language Inference Framework for Dialogue Symptom Status Recognition]() Collection index: [llms.txt]() Data updated: 2026-01-02T02:40:09.100Z Authors: Wei Chen, Shiqi Wei, Zhongyu Wei, Xuanjing Huang Published: 2023-05-26T11:23:26.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Symptom Status Recognition on CMDD 1.0 (test)](): Symptom Status Recognition; CMDD 1.0 (test); Window-Level Precision: 84.17. ## Original sources and implementation [Original paper]() [PDF]()