Supervised attention for answer selection in community question answering

Abstract

Answer selection is an important task in community Question Answering (cQA). In recent years, attention-based neural networks have been extensively studied in various natural language processing problems, including question answering. This paper explores match-LSTM for answer selection in cQA. A lexical gap in cQA is more challenging as questions and answers typical contain multiple sentences, irrelevant information, and noisy expressions. In our investigation, word-by-word attention in the original model does not work well on social question-answer pairs. We propose integrating supervised attention into match-LSTM. Specifically, we leverage lexical semantic from external to guide the learning of attention weights for question-answer pairs. The proposed model learns more meaningful attention that allows performing better than the basic model. Our performance is among the top on SemEval datasets.