Morocco

Arabic dialect↔English machine translation remains difficult due to extreme dialect variation, inconsistent orthography, and limited parallel data. Moreover, dialect translation is often needed in remote regions or by economically-disadvantaged communities, which often operate in compute-constrained or offline settings. Motivated by these concerns, in this paper we explore optimizing Arabic dialect↔English translators that run over small LLMs, which could be implemented on small offline devices.

We show that reasoning-oriented reinforcement learning can substantially improve small multilingual LLMs for Arabic dialect translation. Using the MADAR corpus, small Qwen-2.5 models trained with a think-then-translate template and optimized with Group-Relative Policy Optimization using a SacreBLEU reward outperform a much larger 7B baseline trained with supervised fine-tuning. The dialect-to-English BLEU score more than doubles from 17.4 to 34.9, while the English-to-dialect COMET score improves from 0.57 to 0.73.

EACL 2026

Arabic Dialect Translation with Small LLMs: Enhancing through Reasoning-Oriented Reinforcement Learning

reasoning in llms

arabic dialect translation

small llms

policy optimization

reward modeling

low-resource machine translation

multilingual nlp

large language models

machine translation

reinforcement learning

## Welcome to EMNLP 2022!
I am delighted to welcome you to EMNLP 2022! I believe this conference has been complicated beyond any precedent. Over the past year, it’s been thrilling to see the organization team approach each new puzzle with creativity and enthusiasm. We hope that those participating in Abu Dhabi as well as those joining remotely will leave the conference feeling newly inspired by the program and newly connected to our ever-growing community. Following EMNLP 2021 and major NLP conferences since, EMNLP 2022 is “hybrid,” serving both virtual and in-person participants.

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Welcome to the 19th Conference of the European Chapter of the Association for Computational Linguistics (EACL). EACL 2026 will be held in Rabat, Morocco, from March 24–29, 2026. 

This paper introduces resources for the computational study of scientific exegesis (Tafsir Ilmi): a structured ontology, a curated dataset of 194 scientifically relevant Quranic verses linked to 260 exegetical records from two authoritative Tafsir books, and an annotation framework that organizes scientific references by topic and sequential context. Existing Quranic resources treat verses as unstructured text, losing the logical order and causal relationships of scientific concepts documented in exegesis. To address this, we present QurSci-Onto, a three-layer ontology that categorizes verses by scientific domain, links them to authoritative Tafsir, and provides a framework for representing sequential processes through stage-based annotations. Our dataset includes page-level citations and covers 8 major scientific topics across 74 nodes. While the full corpus is tagged with broad categories and scientific topics, a specialized subset features granular node-level mappings to capture complex scientific narratives. We release QurSci-Onto as a foundational resource for Arabic semantic NLP and demonstrate that it enables significant improvements in semantic retrieval over unstructured baselines.

QurSci-Onto: A Hierarchical Ontology and Dataset for Scientiﬁc Exegesis in the Quran

The availability of large annotated corpora remains a major challenge for the development of natural language processing systems for under-resourced languages such as Arabic. In this paper, we present two annotated corpora dedicated to Modern Standard Arabic. These corpora are open-source and freely available on the Hugging Face platform. The first corpus, annotated by theme and designed to provide a balanced representation of contemporary Arabic usage, comprises approximately one hundred million words collected from diverse sources covering multiple domains and geographical regions. The second corpus, containing approximately one million words, is a sub-corpus extracted from the first. It was annotated with lemma tags using a semi-automatic approach that combines automatic annotation with the Alkhalil lemmatizer and MADAMIRA, followed by manual validation.

Alkhalil Corpus: An Open-Source Thematic and Lemmatized Corpus for Modern Standard Arabic

Back-of-the-book indexes (BoBIs) are crucial for book readability.
However, their manual creation is laborious and error prone.
In this paper,
we introduce ArBoBIM to automate
BoBI extraction and review processes for
Arabic books. Given a book with a corresponding BoBI, ArBoBIM extracts BoBI terms and identifies their occurrences and aligns those across
several versions of the book.
ArBoBIM first defines a pool of
candidates for each term by leveraging
noun phrases and named entities.
ArBoBIM leverages several metrics, including exact matches, morpho-lexical similarity, and semantic similarity, to determine the best candidates.
We empirically fine-tuned thresholds for ArBoBIM and achieve an F1-score of 0.94 (precision= 0.97, recall=0.91).
These results are significantly better than baseline results, and top LLM based results with lower computational cost and no publishing house IP risks.
Additionally, with ArBoBIM, over 500 books have been processed, resulting in the ArBoBIMap dataset, containing books, their terms, occurrences, and various metadata related to them, to be made available for the public. This dataset is used to train a model to identify if a term, given its features, should be added to the back-of-the-book index of a specific book. The model achieves an F1-score of 0.91 (precision = 0.97, recall = 0.85).

Back-of-the-Book Index Automation for Arabic Documents

Automatic Speech Recognition (ASR) has achieved strong performance in high-resource languages; however, Dialectal Arabic remains significantly under-resourced. This gap is particularly evident in Oman, where Arabic exhibits substantial sociolinguistic variation shaped by settlement patterns between sedentary (Hadari) and nomadic (Badu) communities, which are often overlooked by urban-centric or generalized Gulf Arabic datasets. We introduce OMAN-SPEECH, a sociolinguistically stratified spoken corpus for Omani Arabic comprising approximately 40 hours of spontaneous and semi-spontaneous speech from 32 speakers across 11 Wilayats. The corpus is balanced to capture regional and lifestyle variation and is annotated at the sentence level with Arabic transcription, English translation, and phonetic transcription using the International Phonetic Alphabet (IPA) through a human-in-the-loop annotation pipeline. OMAN-SPEECH provides a foundational resource for evaluating ASR and related speech technologies on Omani and Gulf Arabic varieties and supports more granular modeling of regional dialectal variation.

OMAN-SPEECH: A Multi-Layer Annotated Speech Corpus for Omani Arabic Dialects

We present HALA, a family of Arabic-centric instruction and translation models built with our translate-and-tune pipeline. We first compress a strong AR↔EN teacher to FP8 (yielding ~2× higher throughput with no quality loss) and use it to create high-fidelity bilingual supervision. A lightweight language model LFM2–1.2B is then fine-tuned on this data and used to translate high-quality English instruction sets into Arabic, producing a million-scale corpus tailored to instruction following. We train HALA models at 350M, 700M, 1.2B, and 9B parameters, and apply slerp merging to balance Arabic specialization with base-model strengths. On Arabic-centric benchmarks, HALA achieves state-of-the-art results within both the "nano" (≤2B) and "small" (7–9B) categories, outperforming their bases. We are committed to release models, data, evaluation, and recipes to accelerate research in Arabic NLP.

Hala Technical Report Building Arabic-Centric Instruction & Translation Models at Scale

This paper introduces an industry level citation element extractor from Arabic text. Citation element extraction enables editorial task automation for publishing houses, creation of citation networks,
and automatic citation analytics for impact analysis firms. Citation library tools help users manage their citations. However, for Arabic, these tools lack basic support to identify and extract citation elements. Consequently, researchers, editors and reviewers manually manage Arabic citations tasks. We present a novel Arabic citation element dataset, use it to train a citation element extraction model,
and use named entity recognition, morphological analysis, and keyword detection to improve the results for practical use. The paper reports industry ready performance with F1 scores ranging between .80 and .95 for interesting citation elements.

Arabic Citation Parsing using Part of Speech and Named Entity Recognition

Arabic Named Entity Recognition (ANER) presents challenges due to its linguistic characteristics (Qu et al., 2023). While Transformer models have advanced ANER, evaluation still relies heavily on aggregate metrics like F1 score that obscure the interplay between data characteristics, model behaviour, and error patterns. We present DeformAR, a diagnostic visual analytics framework for evaluating and diagnosing Arabic NER systems through structured, component-level analysis and interpretability. DeformAR integrates quantitative metrics with interactive visualizations to support systematic error analysis, dataset and model debugging. A key contribution is span-level error categorization—implemented by extending seqeval—enabling diagnosis of boundary, exclusion, and type errors that aggregate scores conceal. In a case study on ANERCorp, DeformAR identifies annotation mistakes, model calibration issues, and subcomponent interaction effects. To our knowledge, this is the first open-source framework for component-level diagnostic evaluation and interpretability in Arabic NER.

DeformAR: A Visual Analytics Framework for Evaluation of Arabic Named Entity Recognition

The spoken Arabic exhibits substantial dialectal variation in the Arabic-speaking world. This paper presents a corpus-based analysis of Arabic dialectal variation using the SADA corpus, examining lexical, morphosyntactic, and discourse-pragmatic patterns across dialects. We combine quantitative frequency-based measures with qualitative linguistic analysis, including keyword comparison, distributional profiling, collocational and trigram analyses, and similarity-based clustering. Our results show that Arabic dialects share a substantial common core, while differing systematically in frequent discourse markers, evaluative expressions, and recurrent phraseological patterns. These findings provide empirical evidence for regional clustering among contemporary dialects and for variation relative to the standard register. The study contributes linguistic insights that support both Arabic dialectology and the development of dialect-aware NLP systems.

A Corpus-Based Investigation of Contemporary Arabic Dialects Using the SADA Corpus

Several Quranic morphological corpora have been developed to support Arabic linguistic analysis and NLP applications, yet they often lack full coverage, consistency, or manual verification. We present QAMAR, a morphologically oriented, multi-task corpus derived from the Qur'an. This comprehensive, manually verified resource provides a detailed linguistic layer for every Quranic word, including the Modern Standard Arabic (MSA) equivalent, the stem, the lemma, the root, and the part of speech (POS). QAMAR supports multiple NLP tasks, such as normalization, lemmatization, root extraction, and POS tagging, and serves as a gold-standard reference for Quranic and Arabic NLP research, including corpus-to-corpus evaluation and morphological analyzer benchmarking. The paper details QAMAR's annotation framework, verification process, and resource structure, and reports comparative analyses with existing Quranic morphological resources and outputs produced by current large language models (LLMs).

QAMAR: A New Fully Verified and Accurate Quranic Arabic Morphological Analysis Resource

Automatic detection of toxic and offensive content in Arabic social media is a challenging task due to rich morphology, dialectal variation, and noisy writing styles. While transformer-based language models have achieved strong performance, they often produce uncertain predictions in borderline cases. This paper presents a hybrid framework for Arabic toxicity detection that combines a pretrained Arabic-specific transformer model with a confidence-aware rule-based mechanism. The proposed approach activates automatically induced lexical rules only when the model prediction falls within a predefined gray zone of uncertainty, preserving neural dominance while improving robustness and interpretability. Experiments conducted on a manually annotated dataset of 35,000 Arabic posts demonstrate that the hybrid approach achieves consistent improvements over the baseline model, particularly in reducing false negatives for toxic content. The results indicate that selective rule activation is an effective strategy for enhancing reliability in real-world Arabic social media moderation systems.

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Next from EACL 2026

QurSci-Onto: A Hierarchical Ontology and Dataset for Scientiﬁc Exegesis in the Quran

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Arabic dialect identification using machine learning and transformer-based models: Submission to the NADI 2022 Shared Task