SLS at SemEval-2016 task 3: Neural-based approaches for ranking in community question answering

Abstract

Community question answering platforms need to automatically rank answers and questions with respect to a given question. In this paper, we present the approaches for the Answer Selection and Question Retrieval tasks of SemEval-2016 (task 3). We develop a bag-of-vectors approach with various vector- and text-based features, and different neural network approaches including CNNs and LSTMs to capture the semantic similarity between questions and answers for ranking purpose. Our evaluation demonstrates that our approaches significantly outperform the baselines.