Diverse and Non-redundant Answer Set Extraction on Community QA based on DPPs

• URL: http://arxiv.org/abs/2011.09140v1
• Date: Wed, 18 Nov 2020 07:33:03 GMT
• Title: Diverse and Non-redundant Answer Set Extraction on Community QA based on DPPs
• Authors: Shogo Fujita and Tomohide Shibata and Manabu Okumura
• Abstract summary: In community-based question answering platforms, it takes time for a user to get useful information from among many answers. This paper proposes a new task of selecting a diverse and non-redundant answer set rather than ranking the answers.
• Score: 18.013010857062643
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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