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

Shogo Fujita, Tomohide Shibata, Manabu Okumura


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.
Anthology ID:
2020.coling-main.464
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Editors:
Donia Scott, Nuria Bel, Chengqing Zong
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
5309–5320
Language:
URL:
https://aclanthology.org/2020.coling-main.464
DOI:
10.18653/v1/2020.coling-main.464
Bibkey:
Cite (ACL):
Shogo Fujita, Tomohide Shibata, and Manabu Okumura. 2020. Diverse and Non-redundant Answer Set Extraction on Community QA based on DPPs. In Proceedings of the 28th International Conference on Computational Linguistics, pages 5309–5320, Barcelona, Spain (Online). International Committee on Computational Linguistics.
Cite (Informal):
Diverse and Non-redundant Answer Set Extraction on Community QA based on DPPs (Fujita et al., COLING 2020)
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PDF:
https://aclanthology.org/2020.coling-main.464.pdf