Diverse and Non-redundant Answer Set Extraction on Community QA based on DPPs
Tokyo Institute of Technology（东京工业大学）；Yahoo Japan Corporation（雅虎日本公司）
英文摘要：
In community-based question answering (CQA) platforms, it takes time for a user to get useful information from among many answers. Although one solution is an answer ranking method, the user still needs to read through the top-ranked answers carefully. This paper proposes a new task of selecting a diverse and non-redundant answer set rather than ranking the answers. Our method is based on determinantal point processes (DPPs), and it calculates the answer importance and similarity between answers by using BERT. We built a dataset focusing on a Japanese CQA site, and the experiments on this dataset demonstrated that the proposed method outperformed several baseline methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
