论文标题
中国医学问题答案基于交互式句子表示学习
Chinese Medical Question Answer Matching Based on Interactive Sentence Representation Learning
论文作者
论文摘要
中国医疗问答匹配比用英语的开放域问题答案匹配更具挑战性。即使深度学习方法在提高问题答案匹配的性能方面表现良好，但这些方法仅着眼于句子中的语义信息，同时忽略了问题和答案之间的语义关联，从而导致性能不足。在本文中，我们设计了一系列交互式句子表示模型来解决此问题。为了更好地适应中国的医学提问，并采用不同神经网络结构的优势，我们建议交叉的BERT网络在句子内部提取深层语义信息以及问题和答案之间的语义关联，然后与多尺度的CNNS CNNS网络或Bigru网络相结合，以使Neural Networks的不同结构更加优势，以了解更具语义性描述的句子。 CMEDQA V2.0和CMEDQA V1.0数据集的实验表明，我们的模型大大优于中国医学问题答案匹配的所有现有最新模型。
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.
