  Chinese medical question-answer matching is more challenging than the
open-domain question answer matching in English. Even though the deep learning
method has performed well in improving the performance of question answer
matching, these methods only focus on the semantic information inside
sentences, while ignoring the semantic association between questions and
answers, thus resulting in performance deficits. In this paper, we design a
series of interactive sentence representation learning models to tackle this
problem. To better adapt to Chinese medical question-answer matching and take
the advantages of different neural network structures, we propose the Crossed
BERT network to extract the deep semantic information inside the sentence and
the semantic association between question and answer, and then combine with the
multi-scale CNNs network or BiGRU network to take the advantage of different
structure of neural networks to learn more semantic features into the sentence
representation. The experiments on the cMedQA V2.0 and cMedQA V1.0 dataset show
that our model significantly outperforms all the existing state-of-the-art
models of Chinese medical question answer matching.
