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        <title>Challenges for Toxic Comment Classification: An In-Depth Error Analysis</title>
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        <namePart type="given">Betty</namePart>
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            <title>Proceedings of the 2nd Workshop on Abusive Language Online (ALW2)</title>
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            <namePart type="given">Darja</namePart>
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            <namePart type="given">Ruihong</namePart>
            <namePart type="family">Huang</namePart>
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            <namePart type="given">Vinodkumar</namePart>
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            <namePart type="given">Jacqueline</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T Challenges for Toxic Comment Classification: An In-Depth Error Analysis
%A van Aken, Betty
%A Risch, Julian
%A Krestel, Ralf
%A Löser, Alexander
%Y Fišer, Darja
%Y Huang, Ruihong
%Y Prabhakaran, Vinodkumar
%Y Voigt, Rob
%Y Waseem, Zeerak
%Y Wernimont, Jacqueline
%S Proceedings of the 2nd Workshop on Abusive Language Online (ALW2)
%D 2018
%8 October
%I Association for Computational Linguistics
%C Brussels, Belgium
%F van-aken-etal-2018-challenges
%X 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.
%R 10.18653/v1/W18-5105
%U https://aclanthology.org/W18-5105
%U https://doi.org/10.18653/v1/W18-5105
%P 33-42

Markdown (Informal)

[Challenges for Toxic Comment Classification: An In-Depth Error Analysis](https://aclanthology.org/W18-5105) (van Aken et al., ALW 2018)

• Challenges for Toxic Comment Classification: An In-Depth Error Analysis (van Aken et al., ALW 2018)

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.