Learning Semantic Relatedness in Community Question Answering Using Neural Models

Community Question Answering forums, such as Quora and Stackoverflow contain millions of questions and answers. Automatically finding the relevant questions from the existing questions and finding the relevant answers to a new question are Natural Language Processing tasks. In this paper, we aim to address these tasks, which we refer to as similar-Question Retrieval and Answer Selection. We present a neural-based model with stacked bidirectional LSTMs and MLP to address these tasks. The model generates the vector representations of the question-question or question-answer pairs and computes their semantic similarity scores, which are then employed to rank and predict relevancies. Extensive experiments demonstrate our results outperform the baselines.