A Multi-view Attention Network for Answer Selection Algorithm

• 1、School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876

Abstract：Answer selection is an important sub-task in the field of natural language processing, and it is also an extremely important supporting technology for automatic question answering systems. Since answer selection task mainly solves the semantic matching between question and answer, the attention mechanism could provide an effective way of information interaction, thus becoming an indispensable key technical module in the question answering system. This paper proposes a multi-view attention network which uses multiple attention types(co-attention and self-attention) and multiple attention variants(max pooling, average pooling, soft alignment) to model multi-perspective semantic views, thus improving the completeness and accuracy of 40 semantic encoding. At the same time, in order to eliminate the expensive requirements of architectural engineering and improve the computational efficiency of the algorithm, this paper proposes to re-imagine attention as a form of feature argumentation method, achieving multiple attention casts. The model returns scalar feature using compressed function after soft attention operations, and re-attach it to the original word representation, providing hints with global knowledge and cross-sentence 45 knowledge for subsequent encoding layers, which could improve representation learning. Experiments on the factual based question answering dataset (TrecQA), open-domain dataset (WikiQA), and community question answering dataset (SemEval-2016 CQA and YahooCQA) outperform existing state-of-the-art models and ablation studies prove the effectiveness of the multi-view attention mechanism.

Keywords： artificial intelligence question answering system answer selection attention mechanism