Supervised attention for answer selection in community question answering

Abstract

<span class="fontstyle0">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 </span><span class="fontstyle2">matchLSTM </span><span class="fontstyle0">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 </span><span class="fontstyle2">matchLSTM</span><span class="fontstyle0">. 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.</span> <br /><br />