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        <title>Studying Generalisability across Abusive Language Detection Datasets</title>
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    <name type="personal">
        <namePart type="given">Anupam</namePart>
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            <title>Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)</title>
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            <namePart type="given">Mohit</namePart>
            <namePart type="family">Bansal</namePart>
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    <abstract>Work on Abusive Language Detection has tackled a wide range of subtasks and domains. As a result of this, there exists a great deal of redundancy and non-generalisability between datasets. Through experiments on cross-dataset training and testing, the paper reveals that the preconceived notion of including more non-abusive samples in a dataset (to emulate reality) may have a detrimental effect on the generalisability of a model trained on that data. Hence a hierarchical annotation model is utilised here to reveal redundancies in existing datasets and to help reduce redundancy in future efforts.</abstract>
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    <identifier type="doi">10.18653/v1/K19-1088</identifier>
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%0 Conference Proceedings
%T Studying Generalisability across Abusive Language Detection Datasets
%A Swamy, Steve Durairaj
%A Jamatia, Anupam
%A Gambäck, Björn
%Y Bansal, Mohit
%Y Villavicencio, Aline
%S Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F swamy-etal-2019-studying
%X Work on Abusive Language Detection has tackled a wide range of subtasks and domains. As a result of this, there exists a great deal of redundancy and non-generalisability between datasets. Through experiments on cross-dataset training and testing, the paper reveals that the preconceived notion of including more non-abusive samples in a dataset (to emulate reality) may have a detrimental effect on the generalisability of a model trained on that data. Hence a hierarchical annotation model is utilised here to reveal redundancies in existing datasets and to help reduce redundancy in future efforts.
%R 10.18653/v1/K19-1088
%U https://aclanthology.org/K19-1088
%U https://doi.org/10.18653/v1/K19-1088
%P 940-950

Markdown (Informal)

[Studying Generalisability across Abusive Language Detection Datasets](https://aclanthology.org/K19-1088) (Swamy et al., CoNLL 2019)

• Studying Generalisability across Abusive Language Detection Datasets (Swamy et al., CoNLL 2019)

ACL

• Steve Durairaj Swamy, Anupam Jamatia, and Björn Gambäck. 2019. Studying Generalisability across Abusive Language Detection Datasets. In Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL), pages 940–950, Hong Kong, China. Association for Computational Linguistics.