联合分类与匹配的FAQ问答模型

Combining Text Classification and Text Matching for FAQ-Based Question Answering

文本分类或文本匹配是解决基于常见问题和解答(FAQ)问答的2个途径.单独使用分类方法不能有效利用标准问题本身的信息,而单独使用匹配方法时,负样本的选择很困难,为此,提出一类将文本分类和文本匹配方法相结合的模型,不仅能选择真正需要区分的负例,并且能够有效利用标准问题的信息.实验结果表明,提出的模型在多个FAQ问答数据上能达到最好性能.

Text classification and text matching are two ways to solve the frequently asked questions(FAQ)-based question answering.However,using the text classification method alone cannot effectively utilize the information of the standard question itself.When the text matching method is used alone,the selection of the negative sample is a very difficult problem.A series of models that combine text classification and text matching methods are proposed,which not only can select negative examples that really need to be distinguished,but also can effectively use the information of standard questions.Experiments show that the proposed models achieve the best performance on multiple FAQ-based question answering data.