Enhancing Document-Based Question Answering via Interaction Between Question Words and POS Tags

The document-based question answering is to select the answer from a set of candidate sentence for a given question. Most Existing works focus on the sentence-pair modeling, but ignore the peculiars of question-answer pairs. This paper proposes to model the interaction between question words and POS tags, as a special kind of information that is peculiar to question-answer pairs. Such information is integrated into a neural model for answer selection. Experimental results on DBQA Task have shown that our model has achieved better results, compared with several state-of-the-art systems. In addition, it also achieves the best result on NLPCC 2017 Shared Task on DBQA.