A Novel Bidirectional LSTM and Attention Mechanism Based Neural Network for Answer Selection in Community Question Answering |
| |
Authors: | Bo Zhang Haowen Wang Longquan Jiang Shuhan Yuan Meizi Li |
| |
Affiliation: | 1.School of Information Engineering, South West University of Science and Technology, Mianyang, 621010, China.2 School of Computer Science, Sichuan University of Science and Engineering, Zigong, 643000, China.3 Department of Network Information Management Center, Sichuan University of Science and Engineering, Zigong, 643000, China.4 Alhamd Islamic University, Balochistan, Pakistan. |
| |
Abstract: | Deep learning models have been shown to have great advantages in answer selection tasks. The existing models, which employ encoder-decoder recurrent neural network (RNN), have been demonstrated to be effective. However, the traditional RNN-based models still suffer from limitations such as 1) high-dimensional data representation in natural language processing and 2) biased attentive weights for subsequent words in traditional time series models. In this study, a new answer selection model is proposed based on the Bidirectional Long Short-Term Memory (Bi-LSTM) and attention mechanism. The proposed model is able to generate the more effectivequestion-answer pair representation. Experiments on a question answering dataset thatincludes information from multiple fields show the great advantages of our proposed model. Specifically, we achieve a maximum improvement of 3.8% over the classical LSTM model in terms of mean average precision. |
| |
Keywords: | Question answering answer selection deep learning Bi-LSTM attention mechanisms. |
|
| 点击此处可从《计算机、材料和连续体(英文)》浏览原始摘要信息 |
|
点击此处可从《计算机、材料和连续体(英文)》下载全文 |
|