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| Autores principales: | , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2503.06534 |
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| _version_ | 1866909531925118976 |
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| author | Tan, Xingwei Lyu, Chen Umer, Hafiz Muhammad Khan, Sahrish Parvatham, Mahathi Arthurs, Lois Cullen, Simon Wilson, Shelley Jhumka, Arshad Pergola, Gabriele |
| author_facet | Tan, Xingwei Lyu, Chen Umer, Hafiz Muhammad Khan, Sahrish Parvatham, Mahathi Arthurs, Lois Cullen, Simon Wilson, Shelley Jhumka, Arshad Pergola, Gabriele |
| contents | Detecting toxic language including sexism, harassment and abusive behaviour, remains a critical challenge, particularly in its subtle and context-dependent forms. Existing approaches largely focus on isolated message-level classification, overlooking toxicity that emerges across conversational contexts. To promote and enable future research in this direction, we introduce SafeSpeech, a comprehensive platform for toxic content detection and analysis that bridges message-level and conversation-level insights. The platform integrates fine-tuned classifiers and large language models (LLMs) to enable multi-granularity detection, toxic-aware conversation summarization, and persona profiling. SafeSpeech also incorporates explainability mechanisms, such as perplexity gain analysis, to highlight the linguistic elements driving predictions. Evaluations on benchmark datasets, including EDOS, OffensEval, and HatEval, demonstrate the reproduction of state-of-the-art performance across multiple tasks, including fine-grained sexism detection. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_06534 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SafeSpeech: A Comprehensive and Interactive Tool for Analysing Sexist and Abusive Language in Conversations Tan, Xingwei Lyu, Chen Umer, Hafiz Muhammad Khan, Sahrish Parvatham, Mahathi Arthurs, Lois Cullen, Simon Wilson, Shelley Jhumka, Arshad Pergola, Gabriele Computation and Language Detecting toxic language including sexism, harassment and abusive behaviour, remains a critical challenge, particularly in its subtle and context-dependent forms. Existing approaches largely focus on isolated message-level classification, overlooking toxicity that emerges across conversational contexts. To promote and enable future research in this direction, we introduce SafeSpeech, a comprehensive platform for toxic content detection and analysis that bridges message-level and conversation-level insights. The platform integrates fine-tuned classifiers and large language models (LLMs) to enable multi-granularity detection, toxic-aware conversation summarization, and persona profiling. SafeSpeech also incorporates explainability mechanisms, such as perplexity gain analysis, to highlight the linguistic elements driving predictions. Evaluations on benchmark datasets, including EDOS, OffensEval, and HatEval, demonstrate the reproduction of state-of-the-art performance across multiple tasks, including fine-grained sexism detection. |
| title | SafeSpeech: A Comprehensive and Interactive Tool for Analysing Sexist and Abusive Language in Conversations |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2503.06534 |