CSDM: A context-sensitive deep matching model for medical dialogue information extraction

ontext-ensitive eep atching model for medical information extraction in multi-turn dialogue, dubbed CSDM. Specifically, we first introduce a multi-view aware channel, which exploits multiple mask templates to capture different information in dialogues. Thus, the transition and interaction of speaker roles are considered in the model. Second, we utilize a bi-directional attention mechanism to assess the relative importance of different contexts. Therefore, the proposed model can perceive information of multi-turn dialogue. Extensive experiments on a public benchmark dataset show that our method achieves new state-of-the-art performance, which demonstrates the effectiveness of CSDM.