Translate, Then Detect: Leveraging Machine Translation for Cross-Lingual Toxicity Classification (2025)

Abstract: Multilingual toxicity detection remains a significant challenge due to the scarcity of training data and resources for many languages. While prior work has leveraged the translate-test paradigm to support cross-lingual transfer across a range of classification tasks, the utility of translation in supporting toxicity detection at scale remains unclear.In this work, we conduct a comprehensive comparison of translation-based and language-specific/multilingual classification pipelines.We find that translation-based pipelines consistently outperform out-of-distribution classifiers in 81.3% of cases (13 of 16 languages), with translation benefits strongly correlated with both the resource level of the target language and the quality of the machine translation (MT) system.Our analysis reveals that traditional classifiers continue to outperform LLM-based judgment methods, with this advantage being particularly pronounced for low-resource languages, where translate-classify methods dominate translate-judge approaches in 6 out of 7 cases.We show that MT-specific fine-tuning on LLMs yields lower refusal rates compared to standard instruction-tuned models, but it can negatively impact toxicity detection accuracy for low-resource languages.These findings offer actionable guidance for practitioners developing scalable multilingual content moderation systems.

## introduction detecting instances of toxic, abusive, or hateful content at scale is a challenging problem with important, real-world implications for content moderation. in a multilingual setting, however, toxicity detection is often rendered particularly difficult due to a paucity of labeled data for lower-resourced languages. in parallel, recent years have seen the scaling up of machine translation (mt) systems to ⋆ joint first author in this example, text in spanish (es) is optionally translated to english (en) before classification. in monolingual non-english settings, crosslingual transfer has already proven useful for toxicity detection @xcite , aligning with broader analyses of translation's utility for cross-lingual transfer across a range of classification tasks @xcite @xcite . specifically, @xcite compare translate-test (translating a sample before zero-shot classification) against translate-train (translating a sample before classification with a classifier finetuned on translation data) and find that translate-test is competitive as long as translation quality is sufficient. while cross-lingual classification has been widely studied in other nlp tasks, toxicity detection presents distinctive challenges that warrant separate investigation. toxic language is culturally and contextually grounded, with expressions, slurs, and taboos that often lack direct equivalents across languages, making transfer more brittle than for semantically simpler labels. online toxicity also frequently involves code-switching, orthographic variation, and deliberate obfuscation, which may be less common in other tasks. moreover, toxicity labels are inherently subjective and shaped by cultural norms, leading to potential label drift when transferring across languages. these factors, combined with the high stakes of moderation errors, make cross-lingual transfer in toxicity detection both consequential and scientifically challenging. in this work, we present an empirical exploration of translation for multilingual toxicity detection, through the lens of the practitioner for whom labeled data may be unavailable-a particularly common scenario when working with lowerresourced languages-by comparing translationbased pipelines against a variety of off-the-shelf multi-and monolingual classifiers. across 27 pipelines spanning five mt systems and nine toxicity classifiers-including both traditional classifiers and large language model (llm) judges-we evaluate the benefit of cross-lingual classification in 17 languages with varying levels of resources. our results suggest that leveraging translation is an effective method for multilingual toxicity detection ( §4.1), with benefits scaling in line with increasing language resources and mt system quality ( §4.2). motivated by these results, we study the issue of refusal rates and its mitigation via mt supervised finetuning (mt-sft), as well as the downstream effect of mt-sft on toxicity detection performance ( §4.3). finally, we explore classifying using llm judges and compare them to traditional toxicity classifiers ( §4.4). we conclude with practical recommendations for deploying multilingual toxicity detection systems at scale. ## methods we evaluate the performance of toxicity detection pipelines, where a pipeline comprises a binary toxicity classifier and an optional mt system. in many languages-and particularly for lower-resourced languages-labeled data for toxicity detection is unavailable, precluding the training and deployment of specialized classifiers and motivating the consideration of translation-based pipelines. as such, we are principally interested in comparing pipelines in the following three regimes: ## discussion across ten benchmarks spanning 17 languages, our analysis suggests that translation-based approaches can be successfully leveraged to support multilingual toxicity detection at scale. specifically, we observe that translate-classify pipelines outperform classify (ood), a non-finetuned classifier operating ood (i.e., an off-the-shelf model) in the majority of cases, and can even occasionally outperform classify (id), dedicated finetuned classifiers evaluated id. the relative benefit of using translate-classify over classify pipelines in-creases with both a language's available resources and the quality of the translation system. this may suggest that while translation may be an effective strategy in general, it does have the potential to increase performance disparities between better-and worse-resourced languages. we additionally note that using an mt-finetuned llm for translations can further drive up pipeline performance, in part, by reducing refusal rates, but that this benefit appears to be reserved for higher-resourced languages. finally, we evaluate the utility of an llm judge approach over traditional (e.g., bert-based) classification, finding that in lower-resourced languages, translate-classify consistently outperforms.