A Study of Answer Selection Task Based on Deep Learning Methods

Answer selection task is an important task in question answering systems. In this work, we propose several deep learning methods to address answer selection task. Current answer selection tasks use LSTM networks to learn the contextual information of query and candidate answer sequences, but the LSTM network suffers from the problem of gradient instability and fail to extract local information. Aiming to solve these problems, we first introduce fusion layer with residual ideas to alleviate gradient instability. Then we further introduce CNN networks to capture local n-gram information. In addition, we introduce one-way and two-way attention mechanism respectively, in order to capture the interaction between query and candidate answer, and further improve model performance. Experimental results of two public datasets InsuranceQA and WikiQA show that our methods outperform baseline methods, which conclude the effectiveness of our methods proposed.