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        <title>Diverse and Non-redundant Answer Set Extraction on Community QA based on DPPs</title>
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    <name type="personal">
        <namePart type="given">Manabu</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T Diverse and Non-redundant Answer Set Extraction on Community QA based on DPPs
%A Fujita, Shogo
%A Shibata, Tomohide
%A Okumura, Manabu
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F fujita-etal-2020-diverse
%X 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.
%R 10.18653/v1/2020.coling-main.464
%U https://aclanthology.org/2020.coling-main.464/
%U https://doi.org/10.18653/v1/2020.coling-main.464
%P 5309-5320

Markdown (Informal)

[Diverse and Non-redundant Answer Set Extraction on Community QA based on DPPs](https://aclanthology.org/2020.coling-main.464/) (Fujita et al., COLING 2020)

• Diverse and Non-redundant Answer Set Extraction on Community QA based on DPPs (Fujita et al., COLING 2020)

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.