A Stacked BiLSTM Neural Network Based on Coattention Mechanism for Question Answering

Abstract

Deep learning is the crucial technology in intelligent question answering research tasks. Nowadays, extensive studies on question answering have been conducted by adopting the methods of deep learning. The challenge is that it not only requires an effective semantic understanding model to generate a textual representation but also needs the consideration of semantic interaction between questions and answers simultaneously. In this paper, we propose a stacked Bidirectional Long Short-Term Memory (BiLSTM) neural network based on the coattention mechanism to extract the interaction between questions and answers, combining cosine similarity and Euclidean distance to score the question and answer sentences. Experiments are tested and evaluated on publicly available Text REtrieval Conference (TREC) 8-13 dataset and Wiki-QA dataset. Experimental results confirm that the proposed model is efficient and particularly it achieves a higher mean average precision (MAR) of 0.7613 and mean reciprocal rank (MRR) of 0.8401 on the TREC dataset.