@inproceedings{kobayashi-etal-2021-case,
title = "A Case Study of In-House Competition for Ranking Constructive Comments in a News Service",
author = "Kobayashi, Hayato and
Taguchi, Hiroaki and
Tabuchi, Yoshimune and
Koleejan, Chahine and
Kobayashi, Ken and
Fujita, Soichiro and
Murao, Kazuma and
Masuyama, Takeshi and
Yatsuka, Taichi and
Okumura, Manabu and
Sekine, Satoshi",
editor = "Ku, Lun-Wei and
Li, Cheng-Te",
booktitle = "Proceedings of the Ninth International Workshop on Natural Language Processing for Social Media",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.socialnlp-1.3",
doi = "10.18653/v1/2021.socialnlp-1.3",
pages = "24--35",
abstract = "Ranking the user comments posted on a news article is important for online news services because comment visibility directly affects the user experience. Research on ranking comments with different metrics to measure the comment quality has shown {``}constructiveness{''} used in argument analysis is promising from a practical standpoint. In this paper, we report a case study in which this constructiveness is examined in the real world. Specifically, we examine an in-house competition to improve the performance of ranking constructive comments and demonstrate the effectiveness of the best obtained model for a commercial service.",
}
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<abstract>Ranking the user comments posted on a news article is important for online news services because comment visibility directly affects the user experience. Research on ranking comments with different metrics to measure the comment quality has shown “constructiveness” used in argument analysis is promising from a practical standpoint. In this paper, we report a case study in which this constructiveness is examined in the real world. Specifically, we examine an in-house competition to improve the performance of ranking constructive comments and demonstrate the effectiveness of the best obtained model for a commercial service.</abstract>
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%0 Conference Proceedings
%T A Case Study of In-House Competition for Ranking Constructive Comments in a News Service
%A Kobayashi, Hayato
%A Taguchi, Hiroaki
%A Tabuchi, Yoshimune
%A Koleejan, Chahine
%A Kobayashi, Ken
%A Fujita, Soichiro
%A Murao, Kazuma
%A Masuyama, Takeshi
%A Yatsuka, Taichi
%A Okumura, Manabu
%A Sekine, Satoshi
%Y Ku, Lun-Wei
%Y Li, Cheng-Te
%S Proceedings of the Ninth International Workshop on Natural Language Processing for Social Media
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F kobayashi-etal-2021-case
%X Ranking the user comments posted on a news article is important for online news services because comment visibility directly affects the user experience. Research on ranking comments with different metrics to measure the comment quality has shown “constructiveness” used in argument analysis is promising from a practical standpoint. In this paper, we report a case study in which this constructiveness is examined in the real world. Specifically, we examine an in-house competition to improve the performance of ranking constructive comments and demonstrate the effectiveness of the best obtained model for a commercial service.
%R 10.18653/v1/2021.socialnlp-1.3
%U https://aclanthology.org/2021.socialnlp-1.3
%U https://doi.org/10.18653/v1/2021.socialnlp-1.3
%P 24-35
Markdown (Informal)
[A Case Study of In-House Competition for Ranking Constructive Comments in a News Service](https://aclanthology.org/2021.socialnlp-1.3) (Kobayashi et al., SocialNLP 2021)
ACL
- Hayato Kobayashi, Hiroaki Taguchi, Yoshimune Tabuchi, Chahine Koleejan, Ken Kobayashi, Soichiro Fujita, Kazuma Murao, Takeshi Masuyama, Taichi Yatsuka, Manabu Okumura, and Satoshi Sekine. 2021. A Case Study of In-House Competition for Ranking Constructive Comments in a News Service. In Proceedings of the Ninth International Workshop on Natural Language Processing for Social Media, pages 24–35, Online. Association for Computational Linguistics.