It Takes Three to Tango: Triangulation Approach to Answer Ranking in Community Question Answering

We address the problem of answering new questions in community forums, by selecting suitable answers to already asked questions. We approach the task as an answer ranking problem, adopting a pairwise neural network architecture that selects which of two competing answers is better. We focus on the utility of the three types of similarities occurring in the triangle formed by the original question, the related question, and an answer to the related comment, which we call relevance, relatedness, and appropriateness. Our proposed neural network models the interactions among all input components using syntactic and semantic embeddings, lexical matching, and domain-specific features. It achieves state-of-the-art results, showing that the three similarities are important and need to be modeled together. Our experiments demonstrate that all feature types are relevant, but the most important ones are the lexical similarity features, the domain-specific features, and the syntactic and semantic embeddings.