Open Access

ARTICLE

A Novel Bidirectional LSTM and Attention Mechanism Based Neural Network for Answer Selection in Community Question Answering

Bo Zhang¹, Haowen Wang^{1, #}, Longquan Jiang¹, Shuhan Yuan², Meizi Li^{1, *}

1 College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai, 200234, China.
2 The Computer Science and Computer Engineering Department, University of Arkansas, Fayetteville, AR, 72703, USA.
# The author contributes equally to this work and should be considered co-first author.

* Corresponding Author: Meizi Li. Email: .

Computers, Materials & Continua 2020, 62(3), 1273-1288. https://doi.org/10.32604/cmc.2020.07269

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 effective question-answer pair representation. Experiments on a question answering dataset that includes 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.

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Citations