@inproceedings{floto-etal-2023-diffudetox,
title = "{D}iffu{D}etox: A Mixed Diffusion Model for Text Detoxification",
author = "Floto, Griffin and
Abdollah Pour, Mohammad Mahdi and
Farinneya, Parsa and
Tang, Zhenwei and
Pesaranghader, Ali and
Bharadwaj, Manasa and
Sanner, Scott",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-acl.478",
doi = "10.18653/v1/2023.findings-acl.478",
pages = "7566--7574",
abstract = "Text detoxification is a conditional text generation task aiming to remove offensive content from toxic text. It is highly useful for online forums and social media, where offensive content is frequently encountered. Intuitively, there are diverse ways to detoxify sentences while preserving their meanings, and we can select from detoxified sentences before displaying text to users. Conditional diffusion models are particularly suitable for this task given their demonstrated higher generative diversity than existing conditional text generation models based on language models. Nonetheless, text fluency declines when they are trained with insufficient data, which is the case for this task. In this work, we propose DiffuDetox, a mixed conditional and unconditional diffusion model for text detoxification. The conditional model takes toxic text as the condition and reduces its toxicity, yielding a diverse set of detoxified sentences. The unconditional model is trained to recover the input text, which allows the introduction of additional fluent text for training and thus ensures text fluency. Extensive experimental results and in-depth analysis demonstrate the effectiveness of our proposed DiffuDetox.",
}
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<abstract>Text detoxification is a conditional text generation task aiming to remove offensive content from toxic text. It is highly useful for online forums and social media, where offensive content is frequently encountered. Intuitively, there are diverse ways to detoxify sentences while preserving their meanings, and we can select from detoxified sentences before displaying text to users. Conditional diffusion models are particularly suitable for this task given their demonstrated higher generative diversity than existing conditional text generation models based on language models. Nonetheless, text fluency declines when they are trained with insufficient data, which is the case for this task. In this work, we propose DiffuDetox, a mixed conditional and unconditional diffusion model for text detoxification. The conditional model takes toxic text as the condition and reduces its toxicity, yielding a diverse set of detoxified sentences. The unconditional model is trained to recover the input text, which allows the introduction of additional fluent text for training and thus ensures text fluency. Extensive experimental results and in-depth analysis demonstrate the effectiveness of our proposed DiffuDetox.</abstract>
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%0 Conference Proceedings
%T DiffuDetox: A Mixed Diffusion Model for Text Detoxification
%A Floto, Griffin
%A Abdollah Pour, Mohammad Mahdi
%A Farinneya, Parsa
%A Tang, Zhenwei
%A Pesaranghader, Ali
%A Bharadwaj, Manasa
%A Sanner, Scott
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F floto-etal-2023-diffudetox
%X Text detoxification is a conditional text generation task aiming to remove offensive content from toxic text. It is highly useful for online forums and social media, where offensive content is frequently encountered. Intuitively, there are diverse ways to detoxify sentences while preserving their meanings, and we can select from detoxified sentences before displaying text to users. Conditional diffusion models are particularly suitable for this task given their demonstrated higher generative diversity than existing conditional text generation models based on language models. Nonetheless, text fluency declines when they are trained with insufficient data, which is the case for this task. In this work, we propose DiffuDetox, a mixed conditional and unconditional diffusion model for text detoxification. The conditional model takes toxic text as the condition and reduces its toxicity, yielding a diverse set of detoxified sentences. The unconditional model is trained to recover the input text, which allows the introduction of additional fluent text for training and thus ensures text fluency. Extensive experimental results and in-depth analysis demonstrate the effectiveness of our proposed DiffuDetox.
%R 10.18653/v1/2023.findings-acl.478
%U https://aclanthology.org/2023.findings-acl.478
%U https://doi.org/10.18653/v1/2023.findings-acl.478
%P 7566-7574
Markdown (Informal)
[DiffuDetox: A Mixed Diffusion Model for Text Detoxification](https://aclanthology.org/2023.findings-acl.478) (Floto et al., Findings 2023)
ACL
- Griffin Floto, Mohammad Mahdi Abdollah Pour, Parsa Farinneya, Zhenwei Tang, Ali Pesaranghader, Manasa Bharadwaj, and Scott Sanner. 2023. DiffuDetox: A Mixed Diffusion Model for Text Detoxification. In Findings of the Association for Computational Linguistics: ACL 2023, pages 7566–7574, Toronto, Canada. Association for Computational Linguistics.