Answer Selection in Community Question Answering Using LSTM

Abstract

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.