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        <title>Don’t Augment, Rewrite? Assessing Abusive Language Detection with Synthetic Data</title>
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    <abstract>Research on abusive language detection and content moderation is crucial to combat online harm. However, current limitations set by regulatory bodies and social media platforms can make it difficult to share collected data. We address this challenge by exploring the possibility to replace existing datasets in English for abusive language detection with synthetic data obtained by rewriting original texts with an instruction-based generative model.We show that such data can be effectively used to train a classifier whose performance is in line, and sometimes better, than a classifier trained on original data. Training with synthetic data also seems to improve robustness in a cross-dataset setting. A manual inspection of the generated data confirms that rewriting makes it impossible to retrieve the original texts online.</abstract>
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%0 Conference Proceedings
%T Don’t Augment, Rewrite? Assessing Abusive Language Detection with Synthetic Data
%A Casula, Camilla
%A Leonardelli, Elisa
%A Tonelli, Sara
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand and virtual meeting
%F casula-etal-2024-dont
%X Research on abusive language detection and content moderation is crucial to combat online harm. However, current limitations set by regulatory bodies and social media platforms can make it difficult to share collected data. We address this challenge by exploring the possibility to replace existing datasets in English for abusive language detection with synthetic data obtained by rewriting original texts with an instruction-based generative model.We show that such data can be effectively used to train a classifier whose performance is in line, and sometimes better, than a classifier trained on original data. Training with synthetic data also seems to improve robustness in a cross-dataset setting. A manual inspection of the generated data confirms that rewriting makes it impossible to retrieve the original texts online.
%U https://aclanthology.org/2024.findings-acl.669
%P 11240-11247

Markdown (Informal)

[Don’t Augment, Rewrite? Assessing Abusive Language Detection with Synthetic Data](https://aclanthology.org/2024.findings-acl.669) (Casula et al., Findings 2024)

• Don’t Augment, Rewrite? Assessing Abusive Language Detection with Synthetic Data (Casula et al., Findings 2024)

ACL

• Camilla Casula, Elisa Leonardelli, and Sara Tonelli. 2024. Don’t Augment, Rewrite? Assessing Abusive Language Detection with Synthetic Data. In Findings of the Association for Computational Linguistics ACL 2024, pages 11240–11247, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.