Answer Category-Aware Answer Selection for Question Answering

As a key problem in artificial intelligence, question answering (QA) has always been a topic of intensive research. Most existing methods cast question answering as an answer selection task. The size of the candidate answer pool is usually very large, so it is difficult to accurately select the correct answer. One of the solutions is to narrow the range of candidate answer pool based on the category labels of the answers. However, QA tasks in reality usually only provide the category label of the question but not the category label of the answer. Based on this observation, we propose an Answer Category-Aware Answer Selection system (ACAAS), which jointly leverage unlabelled answer data and labelled question category data to generate answer category pseudo-labels in a joint embedding space. Experimental results on two public QA datasets demonstrate the effectiveness of the proposed method.