引入attention机制：采用注意机制使用权重向量来衡量句子不同部分重要性的不同。attention的计算主要思想沿用了AICNN和ABCNN中的几种attention，分别是feature的attention，interaction后新的表示和句子原表示之间的attention。
四、总结与展望 4.1 数据层面建立更加合理的知识库：每个知识点只包含一个意图，且知识点之间没有交叉，歧义，冗余等容易造成混淆的因素
标注：为每个FAQ积累一定数量的有代表性的相似问
后期的持续维护：包括新FAQ发现，原FAQ的合并、拆分、纠正等
4.2 模型层面进一步捕捉syntactic level和semantic level的知识如语义角色标注（SRL, semantic role labelling）和词性标注（POS, part of speech tagging）等，引入到文本的表示之中，提高文本语义匹配的效果
目前大部分检索行问答的工作做的是问题和问题匹配，或是问题和答案匹配。后续可以同时引入问题和答案的信息进行建模，如图：
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