Answer selection is a key part of question answering system, and improving its accuracy is also one of the important contents of question answering. In recent years, answer selection task has achieved good results using deep learning techniques. But there are still some limitations. Among them, model's low utilization of semantic information, lack of local semantic attention, and poor ability to perceive the interaction between sentences are particularly prominent. Regarding the issue above, this paper proposed an answer selection model NHITAS based on Transformer and dual attention fusion. Firstly, this model extracted question categories and keywords for preliminary screening of answers in the information preprocessing part, and introduced external knowledge as additional semantic supplements for sentences. Secondly, it used a hierarchical interactive transformer to model the semantic context of sentences, and proposed two structures to learn semantic features, respectively UP-Transformer and DA-Transformer. Finally, dual attention fusion layers not only filtered sentence noise but also enhanced semantic interaction between question and answer. This paper tested the performance of NHITAS on the WikiQA and TrecQA datasets. The experimental results show that this model can effectively improve the effect of the answer selection task compared with other models.

[2022-06-24] Accepted Paper /doc/2022.04.0183/v1

[2022-11-05] Printed Article /doc/2022.04.0183/v2