SLS at SemEval-2016 Task 3: Neural-based Approaches for Ranking in Community Question Answering Article Swipe

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SemEval Computer science Question answering Ranking (information retrieval) Task (project management) Rank (graph theory) Selection (genetic algorithm) Artificial intelligence Information retrieval Similarity (geometry) Natural language processing Semantic similarity Learning to rank Mathematics Economics Management Combinatorics Image (mathematics)

Mitra Mohtarami , Yonatan Belinkov , Wei-Ning Hsu , Yu Zhang , Tao Leí , Kfir Bar , Scott Cyphers , Jim Glass ·

YOU? · · 2016 · Open Access · · DOI: https://doi.org/10.18653/v1/s16-1128 · OA: W2472879408

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 vectorand 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.