@inproceedings{van-aken-etal-2018-challenges,
    title = "Challenges for Toxic Comment Classification: An In-Depth Error Analysis",
    author = {van Aken, Betty  and
      Risch, Julian  and
      Krestel, Ralf  and
      L{\"o}ser, Alexander},
    editor = "Fi{\v{s}}er, Darja  and
      Huang, Ruihong  and
      Prabhakaran, Vinodkumar  and
      Voigt, Rob  and
      Waseem, Zeerak  and
      Wernimont, Jacqueline",
    booktitle = "Proceedings of the 2nd Workshop on Abusive Language Online ({ALW}2)",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/iwcs-25-ingestion/W18-5105/",
    doi = "10.18653/v1/W18-5105",
    pages = "33--42",
    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."
}