Deep Neural Network to Predict Answer Votes on Community Question Answering Sites

2021 ◽  
Author(s):  
Pradeep Kumar Roy
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Question Answering ◽  
Community Question Answering

A Deep Neural Network Framework for English Hindi Question Answering

2020 ◽  
Vol 19 (2) ◽  
pp. 1-22 ◽  
Author(s):  
Deepak Gupta ◽  
Asif Ekbal ◽  
Pushpak Bhattacharyya
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Question Answering

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

2020 ◽  
Vol 62 (3) ◽  
pp. 1273-1288
Author(s):  
Zhang Bo ◽  
Wang Haowen ◽  
Jiang Longquan ◽  
Yuan Shuhan ◽  
Li Meizi
Keyword(s):  
Neural Network ◽  
Question Answering ◽  
Attention Mechanism ◽  
Community Question Answering ◽  
Bidirectional Lstm

Answer Selection in Community Question Answering Using LSTM

2021 ◽  
pp. 153-165
Author(s):  
Saman Qureshi ◽  
Sri Khetwat Saritha ◽  
D. Kishan
Keyword(s):  
Neural Network ◽  
Question Answering ◽  
Attention Mechanism ◽  
Generalization Ability ◽  
Mechanism Model ◽  
Community Question Answering ◽  
Informative Part ◽  
External Sources

Websites like Quora, Yahoo! Answers, and Reddit are examples of community question answering (CQA) systems that enable users to ask questions as well as to answer questions. Answer selection is the most challenging task in CQA systems to get the good and relevant answer for the user questions. The shortcomings in the current approaches are lexical gap between text pairs, dependency on external sources, and manual features which lead to lack of generalization ability. These shortcomings are resolved by already proposed work, but they lack generalization, and their performance is not satisfying. Whereas to focus on rich quality answers, attention mechanism can be integrates with neural network. This chapter proposes two models BLSTM and BLSTM with attention mechanism. Attention mechanism aligns question to the answer with the answer's more informative part. So, when it is applied in the model, BLSTM with attention mechanism model surpasses the top approaches.

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