Detoxify- IT : An I talian Parallel Dataset for Text Detoxification (2025)

Abstract: Toxic language online poses growing challenges for content moderation. Detoxification, which rewrites toxic content into neutral form, offers a promising alternative but remains underexplored beyond English. We present Detoxify-IT, the first Italian dataset for this task, featuring toxic comments and their human-written neutral rewrites. Our experiments show that even limited fine-tuning on Italian data leads to notable improvements in content preservation and fluency compared to both multilingual models and LLMs used in zero-shot settings, underlining the need for language-specific resources. This work enables detoxification research in Italian and supports broader efforts toward safer, more inclusive online communication.

## introduction online discourse in recent years has become increasingly polluted with toxic language, including hate speech, insults, threats, and other forms of verbal aggression. while automatic systems for content moderation have advanced significantly, most research and resources in this area remain disproportionately focused on english. detoxification, i.e. the task of rewriting toxic content into a more neutral or non-offensive version, has emerged as a promising alternative or complement to traditional moderation, especially when aiming to preserve user intent while reducing harm. however, the development of robust detoxification systems critically depends on the availability of high-quality, language-specific parallel corpora of toxic and detoxified texts. we adopt the definition in this paper, we introduce the first resources and methods for automatic detoxification in italian, a language for which there is currently no prior work in this task. we present detoxify-it, a publicly available parallel corpus containing toxic user-generated comments paired with their manually rewritten, non-toxic versions. by releasing this dataset, we aim to (i) enable the training and evaluation of detoxification systems for italian, (ii) foster multilingual and cross-lingual research on toxicity mitigation, and (iii) contribute to the broader goal of building safer, more inclusive online environments across languages. contributions our contributions are as follows: ## detoxify-it this section presents detoxify-it, a newly created dataset for detoxifying italian toxic content, consisting of 600 user posts manually rewritten into non-toxic versions. the posts are drawn from three main italian-language sources: two tweet-based datasets focused on misogyny and homotransphobia detection and a toxicity detection dataset composed of wikipedia comments. below, we describe the original datasets and the processing and filtering steps used to build the source material for detoxify-it. we selected these datasets based on the availability of italian-language resources and with the goal of varying both the target groups (women and the lgbtqia+ community) and the domains (twitter and wikipedia). ## experiments given the lack of publicly available detoxification models specifically trained for italian, we explored several strategies. we started with simple baselines such as toxic word deletion. then, we evaluated existing models used both in their original form and with translation-based preprocessing, and further fine-tune one of them. finally, we assessed the zero-shot capabilities of large language models (llms) for the detoxification task. ## results backtranslation (en) ma cosa, penso che sia il webmaster in persona ora, grande è solo girare la corda. backtranslation (es) ma cosa, immagino che ora è il webmaster stesso, fantastico sta solo correndo la corda. @xcite mi sa che ora è il webmaster stesso, ottimo sta solo filando la corda. @xcite ma che mi sa che ora è il webmaster stesso, ottimo sta solo filando la corda. fine-tuning ma che cavolo, mi sa che ora è il webmaster stesso, ottimo sta solo filando la corda. ## related work in the domain of modern nlp for proactive content moderation @xcite , various strategies have been developed, ranging from finegrained abusive language classification and text detoxification to counter speech generation. while counter speech with proactive, reasoned arguments is often most effective in addressing severe hate speech, text detoxification techniques are particularly well-suited for moderating content containing profane or offensive language, such as in applications aimed at creating safer online environments for youth @xcite . modern text style transfer (tst) approaches are typically categorized into supervised and unsupervised methods @xcite . unsupervised models @xcite have shown strong performance in control-lable generation. recent work has also explored diffusion models for detoxification @xcite and llms for tasks like paraphrasing and detoxification @xcite . however, models trained on parallel corpora often outperform llms, which may hallucinate @xcite @xcite . multilingual tst has expanded to a range of languages beyond english. sentiment transfer has been developed for indian languages @xcite , while formality transfer has been extended to brazilian portuguese, french, and italian @xcite , and to japanese @xcite . detoxification, initially applied to english @xcite , has recently been adapted for russian, ukrainian, and spanish @xcite . while many approaches explored modern llms for detoxification on existing and new languages @xcite @xcite , still there performance is far from being on par with human annotations. thus, language and cultural specific datasets are highly required for effective proactive text detoxification solutions. ## conclusions this paper presents the first resource for automatic detoxification of italian texts, introducing detoxify-it, a manually curated parallel corpus of toxic and detoxified texts. our evaluation of a variety of approaches demonstrates that fine-tuning a multilingual model with even a small amount of italian data substantially improves content preservation and fluency, albeit with some trade-off in detoxification strength.