2023/09/12 by Yufeng Zhang, Mengxiang Wang, Zhang, Yufeng +4 · 1 voice
Computer Science · #Advanced Text Analysis Techniques #Artificial Intelligence (cs.AI) #Artificial intelligence #Benchmark (surveying) #Computation and Language (cs.CL) #Computer science #Construct (python library) #Data science #FOS: Computer and information sciences #Information retrieval #Natural Language Processing Techniques #Natural language processing #Perspective (graphical) #Phone #Question answering #Relevance (law) #Set (abstract data type) #Task (project management) #Topic Modeling #Variety (cybernetics) #Viewpoints #cs.AI #cs.CL
paper · pdf · doi:10.48550/arxiv.2309.05938
openalex publication_date 2023/09/12 · arxiv published 2023/09/12 · arxiv updated 2023/10/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.