A QA System Based on Bidirectional LSTM with Text Similarity Calculation Model

Abstract

The development of deep learning in recent years has led to the development of natural language processing [1]. Question answering (QA) system is an important branch of natural language processing. It benefits from the application of neural networks and therefore its performance is constantly improving. The application of recurrent neural networks (RNN) and long short-term memory (LSTM) networks are more common in natural language processing. Inspired by the work of machine translation, this paper built an intelligent QA system based on the specific areas of the extension service. After analyzing the shortcomings of the RNN and the advantages of the LSTM network, we choose the bidirectional LSTM. In order to improve the performance, this paper add text similarity calculation in the QA system. At the end of the experiment, the convergence of the system and the accuracy of the answer to the question showed that the performance of the system is good.