Morocco

This work presents an evaluation of large language models (LLMs) for English to dialectal Arabic machine translation on the MADAR dataset. We evaluate both translation directions (English to Arabic and vice-versa) on 16 Arabic dialects. Our experiments cover a diverse set of models, including specialized Arabic models (Jais, Nile), multilingual models (Gemma, Command-R, Mistral, Aya), and commercial APIs (GPT-4.1). We employ multiple evaluation metrics: BLEU, CHRF, COMET (both reference-based and reference-less variants) and GEMBA (LLM-as-a-judge), as well as a small-scale manual evaluation, to assess translation quality. We discuss the challenges of automatic MT evaluation, especially in the context of Arabic dialects. We also evaluate the ability of LLMs to classify the dialect used in a text. The study offers insights into the capabilities and limitations of current LLMs for dialectal Arabic machine translation, particularly highlighting the difficulty of handling dialectal diversity.

EACL 2026

Current state of LLMs for Arabic dialectal machine translation

dialects

arabic

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workshop paper

#### *Message from the General Chair, Aline Villavicencio*
I’m delighted and honoured to welcome you to the 19th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2026), taking place in the beautiful city of Rabat, in Morocco, in March 24-29, 2026. EACL is the flagship European conference of the Association and EACL 2026 proudly continues our field’s tradition of excellence in scholarship, innovation, and inclusivity. I am deeply grateful to the many volunteers whose dedication, generosity, and tireless efforts have made this conference possible.
For the first time EACL is being hosted in the African continent. This is an important milestone for our community, and we are grateful to our Moroccan hosts for enabling this historic moment by bringing this edition of EACL to Rabat. We are also delighted that the Second Arabic NLP School is co-located with EACL. We hope attendees enjoy this wonderful opportunity to strengthen ties with the Computational Linguistics communities across the African continent. *[Read full message](https://drive.google.com/file/d/14NlmHvwM6fPJuMmOvVh7K0vtQbEyv3SZ/view?usp=sharing)* 

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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. 

We present AraLingBench, a fully human annotated benchmark for evaluating the Arabic linguistic competence of large language mod- els (LLMs). The benchmark spans five core categories: grammar, morphology, spelling, reading comprehension, and syntax, through 150 expert designed multiple choice questions that directly assess structural language understanding. Evaluating 35 Arabic and bilingual LLMs reveals that current models demonstrate strong surface level proficiency but struggle with deeper grammatical and syntactic reasoning. AraLingBench highlights a persistent gap between high scores on knowledge-based benchmarks and true linguistic mastery, showing that many models succeed through memorization or pattern recognition rather than au- thentic comprehension. By isolating and measuring fundamental linguistic skills, AraLingBench provides a diagnostic framework for developing Arabic LLMs

AraLingBench: A Human-Annotated Benchmark for Evaluating Arabic Linguistic Capabilities of Large Language Models

Arabic language faces technical and cultural challenges, including a lack of high-quality resources and the prevalence of regional dialects, which hinders the development of effective language processing systems. Therefore, the "Murabaa" platform was developed to transform Arabic linguistic knowledge into integrated digital resources. The platform aims to provide accurate digital content and promote the use of Arabic in various fields to bridge the gap between tradition and modernity by offering integrated linguistic resources for developing advanced research tools. The platform provides eight accurate dictionaries in the form of a website and a web application, contributing to the digitization of knowledge and its representation within the framework of standard lexical markup. In this study, we also conduct a quantitative comparison of the resources against similar ones to assess the quality of the linguistic knowledge they provide.

Murabaa: A comprehensiveResource Platform for Arabic Morphology

Medical AI systems increasingly rely on large language models (LLMs), yet their deployment in linguistically diverse regions remains unexplored. We address this gap by introducing U-MIRAGE, the first medical question-answering benchmark for Urdu and Roman Urdu. Urdu is the 11th most spoken language (with over 246 million speakers) worldwide. Our systematic evaluation of six state-of-the-art LLMs reveals three main findings. (1) Performance drops 6-10% points when moving from English to Urdu variants, even though medical knowledge should theoretically transfer across languages. (2) Chain-of-Thought (CoT) prompting improves small models by 8-20% points, while surprisingly the larger models' performance degraded by up to 3%. (3) Quantized small models fail catastrophically in low-resource languages, achieving near-random accuracy regardless of various prompting strategies. These findings challenge core assumptions about multilingual medical AI systems. Roman Urdu consistently outperforms standard Urdu script, suggesting orthographic alignment with pre-training data matters more than linguistic proximity. CoT prompting effectiveness depends critically on model architecture rather than task complexity alone. CoT prompting effectiveness depends critically on model architecture rather than task complexity alone. Our contributions are threefold: (1) U-MIRAGE, (2) systematic benchmarking of LLMs for Urdu and Roman Urdu medical reasoning, and (3) empirical analysis of CoT prompting in low-resource contexts. Our code and datasets are publicly available

U-MIRAGE: Benchmarking Chain-of-Thought Reasoning for Urdu Medical QA

Framing detection in Arabic social media is difficult due to interpretive ambiguity, cultural grounding, and limited reliable supervision. Existing LLM-based weak supervision methods typically rely on label aggregation, which is brittle when annotations are few and socially dependent. We propose a reliability-aware weak supervision framework that shifts the focus from label fusion to data curation. A small multi-agent LLM pipeline—two framers, a critic, and a discriminator—treats disagreement and reasoning quality as epistemic signals and produces instance-level reliability estimates. These estimates guide a QUBO-based subset selection procedure that enforces frame balance while reducing redundancy. Intrinsic diagnostics and an out-of-domain Arabic sentiment transfer test show that the selected subsets are more reliable and encode non-random, transferable structure, without degrading strong text-only baselines.

Optimizing What We Trust: Reliability-Guided QUBO Selection of Multi-Agent Weak Framing Signals for Arabic Sentiment Prediction

We introduce the Tarab Corpus, a large-scale cultural and linguistic resource that brings together Arabic song lyrics and poetry within a unified analytical framework. The corpus comprises 2.56 million verses and more than 13.5 million tokens, making it, to our knowledge, the largest open Arabic corpus of creative text spanning both classical and contemporary production. Tarab is broadly balanced between songs and poems and covers Classical Arabic, Modern Standard Arabic (MSA), and six major regional varieties: Egyptian, Gulf, Levantine, Iraqi, Sudanese, and Maghrebi Arabic. The artists and poets represented in the corpus are associated with 28 modern nation states and multiple historical eras, covering over fourteen centuries of Arabic creative expression from the Pre-Islamic period to the twenty-first century. Each verse is accompanied by structured metadata describing linguistic variety, geographic origin, and historical or cultural context, enabling comparative linguistic, stylistic, and diachronic analysis across genres and time. We describe the data collection, normalisation, and validation pipeline and present baseline analyses for variety identification and genre differentiation. The dataset will be released publicly on GitHub, with the access link omitted for anonymisation.

Tarab: A Multi-Dialect Corpus of Arabic Lyrics and Poetry

Context: Natural Language Processing (NLP) has become an essential field with widespread applications across domains such as Large Language Models (LLMs). One of the core applications of NLP is machine translation (MT). A major challenge in MT is handling out-of-vocabulary (OOV) words and spelling mistakes, which can lead to poor translation quality.
Objective: This study compares traditional text-based embeddings with visual embeddings for English-to-Arabic translation. It investigates the effectiveness of each approach, especially in handling noisy inputs or OOV terms.
Method: Using the IWSLT 2017 English-Arabic dataset, we trained a baseline transformer encoder-decoder model using standard text embeddings and compared it with models using several visual embeddings strategies, including vowel-removal preprocessing and trigram-based image rendering. The translated outputs were evaluated using BLEU scores.
Results: indicate that while traditional embeddings achieve higher BLEU scores on clean data, visual embeddings demonstrate superior robustness to input noise and OOV terms, particularly under high-noise conditions.

Seeing Words Differently: Visual Embeddings for Robust English-Arabic Machine Translation

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.

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

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).

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