A Case Study of In-House Competition for Ranking Constructive Comments in a News Service

Hayato Kobayashi, Hiroaki Taguchi, Yoshimune Tabuchi, Chahine Koleejan, Ken Kobayashi, Soichiro Fujita, Kazuma Murao, Takeshi Masuyama, Taichi Yatsuka, Manabu Okumura, Satoshi Sekine


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.
Anthology ID:
2021.socialnlp-1.3
Volume:
Proceedings of the Ninth International Workshop on Natural Language Processing for Social Media
Month:
June
Year:
2021
Address:
Online
Venues:
NAACL | SocialNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
24–35
Language:
URL:
https://aclanthology.org/2021.socialnlp-1.3
DOI:
10.18653/v1/2021.socialnlp-1.3
Bibkey:
Cite (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.
Cite (Informal):
A Case Study of In-House Competition for Ranking Constructive Comments in a News Service (Kobayashi et al., SocialNLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.socialnlp-1.3.pdf