Dataset Creation for Ranking Constructive News Comments
Soichiro Fujita (Tokyo Tech), Hayato Kobayashi, Manabu Okumura (Tokyo Tech)
The 57th Annual Meeting of the Association for Computational Linguistics (ACL-2019), 2019/7
自然言語処理 (Natural Language Processing) 機械学習 (Machine Learning)
- Ranking comments on an online news service is a practically important task for the service provider, and thus there have been many studies on this task. However, most of them considered users’ positive feedback, such as “Like”-button clicks, as a quality measure. In this paper, we address directly evaluating the quality of comments on the basis of “constructiveness,” separately from user feedback. To this end, we create a new dataset including 100K+ Japanese comments with constructiveness scores (C-scores). Our experiments clarify (a) C-scores are not always related to users’ positive feedback and (b) the performance of pairwise ranking models tends to be more enhanced by the variation in comments than that in articles.
Dataset Creation for Ranking Constructive News Comments（外部サイト／External Site Link）