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        <title>Domain Classification-based Source-specific Term Penalization for Domain Adaptation in Hate-speech Detection</title>
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    <name type="personal">
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    <abstract>State-of-the-art approaches for hate-speech detection usually exhibit poor performance in out-of-domain settings. This occurs, typically, due to classifiers overemphasizing source-specific information that negatively impacts its domain invariance. Prior work has attempted to penalize terms related to hate-speech from manually curated lists using feature attribution methods, which quantify the importance assigned to input terms by the classifier when making a prediction. We, instead, propose a domain adaptation approach that automatically extracts and penalizes source-specific terms using a domain classifier, which learns to differentiate between domains, and feature-attribution scores for hate-speech classes, yielding consistent improvements in cross-domain evaluation.</abstract>
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%0 Conference Proceedings
%T Domain Classification-based Source-specific Term Penalization for Domain Adaptation in Hate-speech Detection
%A Bose, Tulika
%A Aletras, Nikolaos
%A Illina, Irina
%A Fohr, Dominique
%S Proceedings of the 29th International Conference on Computational Linguistics
%D 2022
%8 October
%I International Committee on Computational Linguistics
%C Gyeongju, Republic of Korea
%F bose-etal-2022-domain
%X State-of-the-art approaches for hate-speech detection usually exhibit poor performance in out-of-domain settings. This occurs, typically, due to classifiers overemphasizing source-specific information that negatively impacts its domain invariance. Prior work has attempted to penalize terms related to hate-speech from manually curated lists using feature attribution methods, which quantify the importance assigned to input terms by the classifier when making a prediction. We, instead, propose a domain adaptation approach that automatically extracts and penalizes source-specific terms using a domain classifier, which learns to differentiate between domains, and feature-attribution scores for hate-speech classes, yielding consistent improvements in cross-domain evaluation.
%U https://aclanthology.org/2022.coling-1.578
%P 6656-6666

Markdown (Informal)

[Domain Classification-based Source-specific Term Penalization for Domain Adaptation in Hate-speech Detection](https://aclanthology.org/2022.coling-1.578) (Bose et al., COLING 2022)

• Domain Classification-based Source-specific Term Penalization for Domain Adaptation in Hate-speech Detection (Bose et al., COLING 2022)

ACL

• Tulika Bose, Nikolaos Aletras, Irina Illina, and Dominique Fohr. 2022. Domain Classification-based Source-specific Term Penalization for Domain Adaptation in Hate-speech Detection. In Proceedings of the 29th International Conference on Computational Linguistics, pages 6656–6666, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.