@inproceedings{fujita-etal-2019-dataset,
title = "Dataset Creation for Ranking Constructive News Comments",
author = "Fujita, Soichiro and
Kobayashi, Hayato and
Okumura, Manabu",
editor = "Korhonen, Anna and
Traum, David and
M{\`a}rquez, Llu{\'\i}s",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-1250",
doi = "10.18653/v1/P19-1250",
pages = "2619--2626",
abstract = "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 that C-scores are not always related to users{'} positive feedback, and the performance of pairwise ranking models tends to be enhanced by the variation of comments rather than articles.",
}
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%0 Conference Proceedings
%T Dataset Creation for Ranking Constructive News Comments
%A Fujita, Soichiro
%A Kobayashi, Hayato
%A Okumura, Manabu
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F fujita-etal-2019-dataset
%X 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 that C-scores are not always related to users’ positive feedback, and the performance of pairwise ranking models tends to be enhanced by the variation of comments rather than articles.
%R 10.18653/v1/P19-1250
%U https://aclanthology.org/P19-1250
%U https://doi.org/10.18653/v1/P19-1250
%P 2619-2626
Markdown (Informal)
[Dataset Creation for Ranking Constructive News Comments](https://aclanthology.org/P19-1250) (Fujita et al., ACL 2019)
ACL
- Soichiro Fujita, Hayato Kobayashi, and Manabu Okumura. 2019. Dataset Creation for Ranking Constructive News Comments. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 2619–2626, Florence, Italy. Association for Computational Linguistics.