This paper proposes a new task in the field of Answering Subjective Induction Question on Products (SUBJPQA). The answer to this kind of question is non-unique, but can be interpreted from many perspectives. For example, the answer to 'whether the phone is heavy' has a variety of different viewpoints. A satisfied answer should be able to summarize these subjective opinions from multiple sources and provide objective knowledge, such as the weight of a phone. That is quite different from the traditional QA task, in which the answer to a factoid question is unique and can be found from a single data source. To address this new task, we propose a three-steps method. We first retrieve all answer-related clues from multiple knowledge sources on facts and opinions. The implicit commonsense facts are also collected to supplement the necessary but missing contexts. We then capture their relevance with the questions by interactive attention. Next, we design a reinforcement-based summarizer to aggregate all these knowledgeable clues. Based on a template-controlled decoder, we can output a comprehensive and multi-perspective answer. Due to the lack of a relevant evaluated benchmark set for the new task, we construct a large-scale dataset, named SupQA, consisting of 48,352 samples across 15 product domains. Evaluation results show the effectiveness of our approach.

翻译：本文提出了一项面向产品的主观归纳问题回答（SUBJPQA）领域的新任务。此类问题的答案并非唯一，可从多个角度进行阐释。例如，“手机是否沉重”这一问题存在多种不同观点。令人满意的答案应能综合来自多来源的主观意见，并提供客观知识（如手机重量）。这与传统问答任务截然不同——传统任务中事实性问题的答案是唯一的，且可从单一数据源获取。为应对这一新任务，我们提出了一种三步法：首先，从多个关于事实和观点的知识源中检索所有与答案相关的线索；同时收集隐含的常识性事实以补充必要但缺失的上下文。接着，通过交互注意力机制捕捉这些线索与问题的相关性。最后，设计基于强化学习的摘要生成器聚合所有知识性线索，并借助模板控制的解码器输出全面且多视角的答案。由于缺乏针对该新任务的评估基准集，我们构建了大规模数据集SupQA，涵盖15个产品领域的48,352个样本。评估结果表明了本方法的有效性。