We study the problem of preserving offensive load in LLM-generated translations, focusing on how offensive words retain their load across languages. We draw on HurtNet, a novel language resource consisting of five multilingual (Arabic, French, Bulgarian, Greek, and Italian) dictionaries of hurtful terms, with multiple definitions and examples of usage. We draw on HurtNet to identify offensive senses shared by the 5 languages and the terms that denote them, and feed translations with this knowledge. We evaluate the approach through quantitative and qualitative analysis. A series of knowledge-injection zero-shot translation experiments were carried out with four different prompts that gradually increase injected linguistic knowledge, and three LLMs —an open use, a translation-dedicated, and a commercial one. Our results show that translations better preserving offensive load are produced when the prompt explicitly instructs the model about the translated offensive term and that the method is beneficial for those cases where the literal translation of the term does not preserve its offensive load.

We present BAREC-10M, an expanded version of the Balanced Arabic Readability Evaluation Corpus (BAREC). This new release extends the original 1M-word corpus to 10 million words and broadens its scope to include balanced multi-domain coverage annotated for morphology, syntax, and readability. The corpus integrates 45 sub-corpora drawn from diverse sources, including news, educational materials, literature, children’s texts, and religious discourse. Each text is labeled for domain, readership level, and genre, and automatically analyzed using state-of-the-art morphological and syntactic tools. To enhance coverage of underrepresented varieties, we manually digitized and included children’s materials, magazines, and curriculum-based content. The resulting dataset provides a balanced resource for studying Arabic linguistic variation across styles, audiences, and levels of complexity.

Quranic Arabic poses important challenges for Arabic NLP due to its distinctive morphology, syntax, and orthography. We present MAQAM, a richly annotated Morphosyntactic Arabic Quranic Analysis and Modeling resource covering the entire Quran, providing 22 lexical, inflectional, and clitic features for every word. The resource is constructed through a semi-automatic pipeline combining feature enrichment, morphological synchronization, and expert manual annotation and validation. We further evaluate Quranic morphosyntactic disambiguation under a realistic setting where the input may be represented in either the Uthmani or Imla’i script, and investigate cross-domain transfer between Quranic Arabic and MSA. Our results show that MSA and Quranic resources are complementary for Quranic disambiguation, with the best performance obtained by combining MSA training data, Quran-specific analyzers, and both script representations, whereas for MSA, only integrating Quranic resources provide modest gains. We make MAQAM publicly available.

Complex linguistic phenomena such as stereotypes or irony are still challenging to detect, particularly due to the lower availability of annotated data. In this paper, we explore Back-Translation (BT) as a data augmentation method to enhance such datasets by artificially introducing semantics-preserving variations. We investigate French and Italian as source languages on two multilingual datasets annotated for the presence of stereotypes or irony and evaluate French/Italian, English, andArabic as pivot languages for the BT process. We also investigate cross-translation, i.e., augmenting one language subset of a multilingual dataset with translated instances from the other languages. We conduct an intrinsic evaluation of the quality of back-translated instances, identifying linguistic or translation model-specific errors that may occur with BT. We also perform an extrinsic evaluation of different data augmentation configurations to train a multilingual Transformer-based classifier forstereotype or irony detection on mono-lingual data.