@Article{cmc.2020.07269,
AUTHOR = {Bo Zhang, Haowen Wang, Longquan Jiang, Shuhan Yuan, Meizi Li},
TITLE = {A Novel Bidirectional LSTM and Attention Mechanism Based Neural Network for Answer Selection in Community Question Answering},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {62},
YEAR = {2020},
NUMBER = {3},
PAGES = {1273--1288},
URL = {http://www.techscience.com/cmc/v62n3/38354},
ISSN = {1546-2226},
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.},
DOI = {10.32604/cmc.2020.07269}
}