Challenges for Toxic Comment Classification: An In-Depth Error Analysis

Betty van Aken, Julian Risch, Ralf Krestel, Alexander Löser


Abstract
Toxic comment classification has become an active research field with many recently proposed approaches. However, while these approaches address some of the task’s challenges others still remain unsolved and directions for further research are needed. To this end, we compare different deep learning and shallow approaches on a new, large comment dataset and propose an ensemble that outperforms all individual models. Further, we validate our findings on a second dataset. The results of the ensemble enable us to perform an extensive error analysis, which reveals open challenges for state-of-the-art methods and directions towards pending future research. These challenges include missing paradigmatic context and inconsistent dataset labels.
Anthology ID:
W18-5105
Volume:
Proceedings of the 2nd Workshop on Abusive Language Online (ALW2)
Month:
October
Year:
2018
Address:
Brussels, Belgium
Venue:
ALW
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
33–42
Language:
URL:
https://aclanthology.org/W18-5105
DOI:
10.18653/v1/W18-5105
Bibkey:
Cite (ACL):
Betty van Aken, Julian Risch, Ralf Krestel, and Alexander Löser. 2018. Challenges for Toxic Comment Classification: An In-Depth Error Analysis. In Proceedings of the 2nd Workshop on Abusive Language Online (ALW2), pages 33–42, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Challenges for Toxic Comment Classification: An In-Depth Error Analysis (van Aken et al., ALW 2018)
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PDF:
https://aclanthology.org/W18-5105.pdf