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        <title>Mitigating Biases in Hate Speech Detection from A Causal Perspective</title>
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    <name type="personal">
        <namePart type="given">Zhehao</namePart>
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        <dateIssued>2023-12</dateIssued>
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    <abstract>Nowadays, many hate speech detectors are built to automatically detect hateful content. However, their training sets are sometimes skewed towards certain stereotypes (e.g., race or religion-related). As a result, the detectors are prone to depend on some shortcuts for predictions. Previous works mainly focus on token-level analysis and heavily rely on human experts’ annotations to identify spurious correlations, which is not only costly but also incapable of discovering higher-level artifacts. In this work, we use grammar induction to find grammar patterns for hate speech and analyze this phenomenon from a causal perspective. Concretely, we categorize and verify different biases based on their spuriousness and influence on the model prediction. Then, we propose two mitigation approaches including Multi-Task Intervention and Data-Specific Intervention based on these confounders. Experiments conducted on 9 hate speech datasets demonstrate the effectiveness of our approaches.</abstract>
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    <identifier type="doi">10.18653/v1/2023.findings-emnlp.440</identifier>
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%0 Conference Proceedings
%T Mitigating Biases in Hate Speech Detection from A Causal Perspective
%A Zhang, Zhehao
%A Chen, Jiaao
%A Yang, Diyi
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F zhang-etal-2023-mitigating
%X Nowadays, many hate speech detectors are built to automatically detect hateful content. However, their training sets are sometimes skewed towards certain stereotypes (e.g., race or religion-related). As a result, the detectors are prone to depend on some shortcuts for predictions. Previous works mainly focus on token-level analysis and heavily rely on human experts’ annotations to identify spurious correlations, which is not only costly but also incapable of discovering higher-level artifacts. In this work, we use grammar induction to find grammar patterns for hate speech and analyze this phenomenon from a causal perspective. Concretely, we categorize and verify different biases based on their spuriousness and influence on the model prediction. Then, we propose two mitigation approaches including Multi-Task Intervention and Data-Specific Intervention based on these confounders. Experiments conducted on 9 hate speech datasets demonstrate the effectiveness of our approaches.
%R 10.18653/v1/2023.findings-emnlp.440
%U https://aclanthology.org/2023.findings-emnlp.440
%U https://doi.org/10.18653/v1/2023.findings-emnlp.440
%P 6610-6625

Markdown (Informal)

[Mitigating Biases in Hate Speech Detection from A Causal Perspective](https://aclanthology.org/2023.findings-emnlp.440) (Zhang et al., Findings 2023)

• Mitigating Biases in Hate Speech Detection from A Causal Perspective (Zhang et al., Findings 2023)

ACL

• Zhehao Zhang, Jiaao Chen, and Diyi Yang. 2023. Mitigating Biases in Hate Speech Detection from A Causal Perspective. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 6610–6625, Singapore. Association for Computational Linguistics.