  Medical Slot Filling (MSF) task aims to convert medical queries into
structured information, playing an essential role in diagnosis dialogue
systems. However, the lack of sufficient term semantics learning makes existing
approaches hard to capture semantically identical but colloquial expressions of
terms in medical conversations. In this work, we formalize MSF into a matching
problem and propose a Term Semantics Pre-trained Matching Network (TSPMN) that
takes both terms and queries as input to model their semantic interaction. To
learn term semantics better, we further design two self-supervised objectives,
including Contrastive Term Discrimination (CTD) and Matching-based Mask Term
Modeling (MMTM). CTD determines whether it is the masked term in the dialogue
for each given term, while MMTM directly predicts the masked ones. Experimental
results on two Chinese benchmarks show that TSPMN outperforms strong baselines,
especially in few-shot settings.
