BertHANK: hierarchical attention networks with enhanced knowledge and pre-trained model for answer selection

In recent years, Community Question Answering (CQA) becomes increasingly prevalent, because it provides platforms for users to collect information and share knowledge. However, given a question in a CQA system, there are often many different paired answers. It is almost impossible for users to view them item by item and select the most relevant one. Hence, answer selection becomes an important task of CQA. In this paper, we propose a novel solution - BertHANK, which is a hierarchical attention networks with enhanced knowledge and pre-trained model for answer selection. Specifically, in the encoding stage, knowledge enhancement and pre-training model are used for questions and answers, respectively. Further, we adopt multi-attention mechanism, including the cross-attention on question-answer pairs, the inner attention on questions at word level, and the hierarchical inner attention on answers at both word and sentence level, to capture more subtle semantic features. In more details, the cross-attention focuses on capturing interactive information among encoded questions and answers. While the hierarchical inner attention assigns different weights to words in sentences, and sentences in answers, thereby obtaining both global and local information of question-answer pairs. The hierarchical inner attention contributes to select out best-matched answers for specific questions. Finally, we integrate attention-questions and attention-answers to make prediction. The results show that our model achieves state-of-the-art performance on two corpora, SemEval-2015 and SemEval-2017 CQA datasets, outperforming the advanced baselines by a large margin.