Abstract
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.
Highlights
Question answering refers to making the machine understand natural language questions and deliver answers automatically [1]
One of the main challenges in the answer selection task is that the size of the candidate answer pool is usually very large, so it is difficult to accurately select the correct answer from it
Based on the above observations, we propose the Answer Category-aware Answer Selection System (ACAAS)
Summary
Question answering refers to making the machine understand natural language questions and deliver answers automatically [1]. There are many existing machine learning-based question answering methods. Given a question and a set of candidate answers, the system automatically selects the correct answer from the pool of candidate answers [4]. One of the main challenges in the answer selection task is that the size of the candidate answer pool is usually very large, so it is difficult to accurately select the correct answer from it. In most cases, the answer category information is not available, and there is no existing method for answer categorization. Even if the category information of the question is not available, there are existing methods that can accurately categorize questions [9]. Based on the above observations, we propose the Answer Category-aware Answer Selection System (ACAAS). ACAAS selects answers based on the encoded questions, answers, and pseudo-labels. Author et al.: Preparation of Papers for IEEE TRANSACTIONS and JOURNALS results demonstrate the effectiveness of our model
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