Answer selection in community question answering exploiting knowledge graph and context information

With the increasing popularity of knowledge graph (KG), many applications such as sentiment analysis, trend prediction, and question answering use KG for better performance. Despite the obvious usefulness of commonsense and factual information in the KGs, to the best of our knowledge, KGs have been rarely integrated into the task of answer selection in community question answering (CQA). In this paper, we propose a novel answer selection method in CQA by using the knowledge embedded in KGs. We also learn a latent-variable model for learning the representations of the question and answer, jointly optimizing generative and discriminative objectives. It also uses the question category for producing context-aware representations for questions and answers. Moreover, the model uses variational autoencoders (VAE) in a multi-task learning process with a classifier to produce class-specific representations for answers. The experimental results on three widely used datasets demonstrate that our proposed method is effective and outperforms the existing baselines significantly.

Automated summary

This paper proposes a novel method for answer selection in community question answering (CQA) by utilizing knowledge from knowledge graphs (KGs). The paper also introduces a latent-variable model for learning question and answer representations, and uses variational autoencoders (VAE) in a multi-task learning process to generate class-specific representations for answers. Experimental results demonstrate the effectiveness of the proposed method.