Morocco

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.

EACL 2026

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

omani arabic

dialectal arabic

speech corpus

automatic speech recognition

It is my great pleasure and honor to welcome you to the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), held in San Diego, California, USA, July 2–7, 2026. ACL 2026 continues our fields tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.

It is exciting to see the many developments in the field reflected at our conference, such as the advancement of large language models (born out of our community) into many application directions. This is not without challenges, partly due to unsolved problems, and partly due to misuse of the technology. All this is reflected well at ACL 2026. The conference also reflects that the field is not only about advancing technology, but also the science of fundamentally understanding language and how it is use by many different speaker communities. <br>
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ACL 2026

The 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026) will take place in San Diego, California, from July 2 - 7, 2026.

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)*<br><br>

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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 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.

A Hybrid Confidence-Aware Framework for Arabic Toxicity Detection in Social Media

Arabic diacritics encode phonetic information essential for pronunciation, disambiguation, and downstream applications, yet most Arabic ASR systems generate undiacritized output. In this work, we study direct speech-to-diacritized-text recognition using a single-stage ASR pipeline that predicts diacritics jointly with Arabic letters, without text-based post-processing. We evaluate two Arabic-adapted ASR architectures—wav2vec 2.0 XLSR-53 and Whisper-base—under a unified experimental setup on the ClArTTS Classical Arabic dataset. Performance is assessed using surface and lexical WER/CER alongside diacritic error rate (DER) to disentangle base transcription accuracy from diacritic realization. Our results show that Arabic-adapted wav2vec 2.0 achieves substantially lower diacritic error rates than Whisper, indicating stronger exploitation of acoustic cues relevant to vowelization. We further analyze the effect of decoding strategy and provide a detailed breakdown of diacritic errors, highlighting challenges associated with short vowels and morphosyntactic markers. These findings underscore the importance of model architecture and Arabic-specific adaptation for accurate diacritized Arabic ASR.

Arabic-Adapted One-Step Speech-to-Diacritized ASR: Evaluation and Error Analysis

How do Arabic-speaking communities express and engage with psychological stress on social media? We introduce AraStress, the first large-scale Arabic corpus dedicated to psychological stress research, comprising 175,862 public social media posts from 2020 to 2024, covering pandemic and post-pandemic periods.It fills a significant gap in Arabic mental-health NLP resources focused on stress, enabling large-scale analysis of related expressions.Unlike prior work focusing primarily on Twitter and depression or suicidality, AraStress addresses the critical gap in stress-focused resources. Our lexicon-based analysis reveals that stress-related posts elicit predominantly affective engagement and exhibit a hybrid lexical framing that integrates religious and therapeutic language. AraStress provides a foundational resource for culturally grounded computational models of stress detection and digital wellbeing in Arabic-speaking communities.

From Posts to Pressure: An Arabic Dataset about Stress and Mental-Health Monitoring

Stereotype repositories are critical to assess generative AI model safety, but currently lack adequate global coverage. It is imperative to prioritize targeted expansion, strategically addressing existing deficits, over merely increasing data volume. This work introduces a multilingual stereotype resource covering four sub-Saharan African countries that are severely underrepresented in NLP resources: Ghana, Kenya, Nigeria, and South Africa. By utilizing socioculturally-situated, community-engaged methods, including telephonic surveys moderated in native languages, we establish a reproducible methodology that is sensitive to the region's complex linguistic diversity and traditional orality. By deliberately balancing the sample across diverse ethnic and demographic backgrounds, we ensure broad coverage, resulting in a dataset of 3,534 stereotypes in English and 3,206 stereotypes across 15 native languages.

SAFARI: A Community-Engaged Approach and Dataset of Stereotype Resources in the Sub-Saharan African Context

Many languages are predominantly spoken rather than written, and to bring the benefits of LLMs to speakers of these languages, it is essential that models cater to the voice modality. The typical approach is to cascade ASR, LLM and TTS models together, though this results in systems with high latency, making them unsuitable for natural, real-time interaction. We describe results on taking the encoder part of a Whisper-based model trained to recognise ten languages common in Uganda, and using the Ultravox architecture to project its output directly to the input embedding space of a text model based on Qwen 3 32B, also trained to have comprehension of those languages. The result is a speech LLM with high accuracy and very low latency. For most spoken prompts, we can begin streaming a text response within as low as 50 ms, and a speech audio response within around one second, making real-time spoken interaction with an LLM possible for the first time in these languages. The model is available open source.

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

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Arab Voices: Mapping Standard and Dialectal Arabic Speech Technology