Papers with Arabic
Arabic Natural Language Processing (2022.emnlp-tutorials)
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| Challenge: | This tutorial provides background information for system developers and researchers working with Arabic in its various forms. |
| Approach: | This tutorial provides the necessary background information for working with Arabic in its various forms. |
| Outcome: | This tutorial will explain various Arabic linguistic phenomena and review the state-of-the-art in Arabic processing. |
ADIDA: Automatic Dialect Identification for Arabic (N19-4)
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| Challenge: | Demo paper describes a web-based system for automatic dialect identification for Arabic text. |
| Approach: | They present a web-based system for automatic dialect identification for Arabic text that distinguishes between 25 Arab cities and Modern Standard Arabic. |
| Outcome: | The proposed system distinguishes among the dialects of 25 Arab cities (from Rabat to Muscat) and Modern Standard Arabic (MSA). |
The KIND Dataset: A Social Collaboration Approach for Nuanced Dialect Data Collection (2024.eacl-srw)
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Asma Yamani, Raghad Alziyady, Reem AlYami, Salma Albelali, Leina Albelali, Jawharah Almulhim, Amjad Alsulami, Motaz Alfarraj, Rabeah Al-Zaidy
| Challenge: | Nuanced dialects are a linguistic variant that pose several challenges for NLP models and techniques. |
| Approach: | They propose an approach to collect high quality Arabic dialect data by social collaboration . they use short texts to collect Arabic dialects and a KIND corpus . |
| Outcome: | The proposed approach is based on the KIND corpus of Arabic dialect data . it provides a high quality dataset and is versatile enough to be multipurpose . |
Arabic Synonym BERT-based Adversarial Examples for Text Classification (2024.eacl-srw)
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| Challenge: | Often, research studies quantifying the impact of adversarial text attacks have been applied only to models trained in English. |
| Approach: | They propose a word-level study of adversarial text examples in Arabic . they use a synonym attack with a BERT model to assess their robustness . |
| Outcome: | The proposed attack compares Arabic adversarial examples with their original examples and regains 2% accuracy after training. |
NERDz: A Preliminary Dataset of Named Entities for Algerian (2022.aacl-short)
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| Challenge: | NER is a fundamental task in information extraction and natural language processing. |
| Approach: | They propose to build a manually annotated Algerian vernacular dataset using a recent extension to the Algerian NArabizi Treebank. |
| Outcome: | The proposed dataset is the first of its kind for the Algerian vernacular dialect. |
GPTAraEval: A Comprehensive Evaluation of ChatGPT on Arabic NLP (2023.emnlp-main)
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| Challenge: | Our study examines ChatGPT’s performance on Arabic languages and dialectal varieties. |
| Approach: | They conduct a large-scale automated and human evaluation of ChatGPT, encompassing 44 distinct language understanding and generation tasks on over 60 different datasets. |
| Outcome: | The proposed model outperforms smaller models on Arabic dialects compared to GPT-4's Modern Standard Arabic and Dialectal Arabic (DA) |
A description and demonstration of SAFAR framework (2021.eacl-demos)
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Karim Bouzoubaa, Younes Jaafar, Driss Namly, Ridouane Tachicart, Rachida Tajmout, Hakima Khamar, Hamid Jaafar, Lhoussain Aouragh, Abdellah Yousfi
| Challenge: | Existing NLP infrastructures are naming them "toolkit", "platform" and "framework" authors present a monolingual framework dedicated to Arabic language . |
| Approach: | They propose a monolingual framework dedicated to Arabic language . they propose namings for existing infrastructures: "toolkit", "platform" and "framework" |
| Outcome: | The proposed framework is dedicated to Arabic language, especially the modern standard Arabic and Moroccan dialect. |
InkSight: Towards AI-Aided Historical Manuscript Analysis (2026.eacl-demo)
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Andrey Sakhovskiy, Ivan Ulitin, Emilia Bojarskaja, Vladimir Kokh, Ruslan Murtazin, Maxim Novopoltsev, Semen Budennyy
| Challenge: | Large-scale scientific research on medieval Arabic manuscripts remains challenging due to the need for advanced paleographic and linguistic training and the lack of assisting software. |
| Approach: | They propose an end-to-end Arabic manuscript analysis tool for manuscript-based analytics and research hypothesis testing. |
| Outcome: | The proposed tool overcomes the limitations of existing tools and can be used in large-scale scientific research. |
Generating Image Captions in Arabic using Root-Word Based Recurrent Neural Networks and Deep Neural Networks (N18-4)
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| Challenge: | Existing studies on image caption generation in English focus on Western languages, ignoring Semitic and Middle-Eastern languages like Arabic, Hebrew, Urdu and Persian. |
| Approach: | They propose to leverage the critical dependency of Arabic to generate Arabic captions using root-word based Recurrent Neural Network and Deep Neural networks. |
| Outcome: | The proposed model outperforms English-Arabic translated captions on a dataset from newspapers in the Middle East. |
Do Diacritics Matter? Evaluating the Impact of Arabic Diacritics on Tokenization and LLM Benchmarks (2026.findings-eacl)
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| Challenge: | Diacritics can significantly influence language processing tasks in Arabic . their presence can increase subword fragmentation during tokenization, reducing performance . |
| Approach: | They analyze the impact of diacritics on tokenization and benchmark task performance across major Large Language Models. |
| Outcome: | The proposed model is robust to diacritics, but full diacritization leads to token fragmentation and degraded performance. |
AraDIC: Arabic Document Classification Using Image-Based Character Embeddings and Class-Balanced Loss (2020.acl-srw)
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| Challenge: | a novel image-based character embedding framework is used for text classification in Arabic . classical methods require morphological analysis, word segmentation, and hand-crafted feature engineering. |
| Approach: | They propose a novel end-to-end Arabic document classification framework, Arabic document image-based classifier, inspired by image-basic character embeddings. |
| Outcome: | The proposed framework improves on modern standard Arabic, colloquial Arabic, and Classical Arabic. |
Neural Arabic Text Diacritization: State of the Art Results and a Novel Approach for Machine Translation (D19-52)
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| Challenge: | a number of Arabic text diacritizers use diacritics to convey information about meaning of a word . Arabic text to speech (TTS) requires a complex process to determine the correct diacritical for each character . |
| Approach: | They propose to use Arabic diacritization to enhance machine translation models . they propose to build automatic Arabic text diacritics using two approaches . |
| Outcome: | The proposed models are either better or on par with other models, which require language-dependent post-processing steps, unlike ours. |
Camelira: An Arabic Multi-Dialect Morphological Disambiguator (2022.emnlp-demos)
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| Challenge: | Camelira is a web-based Arabic multi-dialect morphological disambiguation tool that covers modern standard Arabic, Egyptian, Gulf, and Levantine. |
| Approach: | They propose a web-based Arabic multi-dialect morphological disambiguation tool that covers modern standard Arabic, Egyptian, Gulf, and Levantine. |
| Outcome: | The proposed tool covers modern standard Arabic, Egyptian, Gulf, and Levantine . it also provides an option to automatically choose an appropriate disambiguator based on the prediction of a dialect identification component. |
Neural Machine Translation for Agglutinative Languages via Data Rejuvenation (2025.acl-srw)
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| Challenge: | Recent years, advances in Neural Machine Translation (NMT) heavily rely on large-scale parallel corpora. |
| Approach: | They propose to combine fine-grained inactive sample identification with target-side rejuvenation to improve translation quality from agglutinative languages. |
| Outcome: | The proposed framework improves on four low-resource agglutinative language tasks. |
ArMATH: a Dataset for Solving Arabic Math Word Problems (2022.lrec-1)
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| Challenge: | This paper is the first to use deep learning methods to solve Arabic MWPs . it is also the first study to use transfer learning to solve MWp across different languages . |
| Approach: | They contribute to the first large-scale dataset for Arabic Math Word Problems . they use deep learning methods to solve Arabic MWPs and a transfer learning model to promote performance . |
| Outcome: | The proposed model improves Arabic MWP solvers by 3% over the existing model. |
A System for Diacritizing Four Varieties of Arabic (D19-3)
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| Challenge: | Short vowels, aka diacritics, are omitted when writing different varieties of Arabic . diacritization is essential for language learning and text-to-speech applications . |
| Approach: | They propose a system for recovering diacritics in Arabic without short vowels . they use a character-based sequence-to-sequence deep learning model . |
| Outcome: | The proposed system beats all previous SOTA systems for Arabic varieties . it uses a character-based sequence-to-sequence deep learning model . |
Mitigating Language-Dependent Ethnic Bias in BERT (2021.emnlp-main)
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| Challenge: | Ethnic bias is one of the most prevalent social stereotypes. |
| Approach: | They propose to use a multilingual model and contextual word alignment to mitigate ethnic bias in monolingual BERT for English, German, Spanish, Korean, Turkish, and Chinese. |
| Outcome: | The proposed methods alleviate ethnic bias in English, German, Spanish, Korean, Turkish, and Chinese using a multilingual model and contextual word alignment of two monolingual models. |
User-Centric Gender Rewriting (2022.naacl-main)
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| Challenge: | Existing systems that embed and amplify gender bias can still exhibit and exacerbate this problem. |
| Approach: | They propose a multi-step system that combines the positive aspects of rule-based and neural rewriting models to provide personalized outputs based on the users’ grammatical gender preferences. |
| Outcome: | The proposed system achieves 88.42 M2 F0.5 on a blind test set and improves over previous work on the first-person-only version of this task by 3.05 absolute increase in M2F0.5. |
Design Challenges in Named Entity Transliteration (C18-1)
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| Challenge: | Named entity transliteration is an important component in many search and language understanding tasks. |
| Approach: | They empirically evaluate a named entity transliteration task using traditional methods . they use a stack of convolutional layers to create a neural network with a new approach . |
| Outcome: | The proposed system outperforms two neural approaches in the named entity transliteration task. |
Fighting the COVID-19 Infodemic: Modeling the Perspective of Journalists, Fact-Checkers, Social Media Platforms, Policy Makers, and the Society (2021.findings-emnlp)
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Firoj Alam, Shaden Shaar, Fahim Dalvi, Hassan Sajjad, Alex Nikolov, Hamdy Mubarak, Giovanni Da San Martino, Ahmed Abdelali, Nadir Durrani, Kareem Darwish, Abdulaziz Al-Homaid, Wajdi Zaghouani, Tommaso Caselli, Gijs Danoe, Friso Stolk, Britt Bruntink, Preslav Nakov
| Challenge: | a dataset of 16K manually annotated tweets is used to analyze disinformation . the democratic nature of social media has raised questions about the quality and the factuality of the information that is shared on these platforms. |
| Approach: | They use a dataset of manually annotated tweets to analyze COVID-19 disinformation . they show that tweets contain fake cures, rumors, conspiracy theories and xenophobia . |
| Outcome: | The proposed dataset shows that it is useful in monolingual vs. multilingual settings. |
Cross-Lingual Transfer from Related Languages: Treating Low-Resource Maltese as Multilingual Code-Switching (2024.eacl-long)
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| Challenge: | Multilingual models exhibit impressive cross-lingual transfer capabilities on unseen languages, but performance is impacted when there is a script disparity with the languages used in the model’s pre-training data. |
| Approach: | They propose a novel method to align a resource-rich language's script with a target language and train a classifier that can make informed decisions regarding the appropriate processing of each token. |
| Outcome: | The proposed model can be used to transfer a language's scripts across multiple languages, but it is suboptimal for mixed languages, where only a subset benefits while the rest is impeded. |
Disentangling Linguistic Relatedness from Task Alignment in Cross-Lingual Transfer (2026.acl-srw)
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| Challenge: | Large language models (LLMs) have advanced natural language processing, yet their benefits remain concentrated in English and a small number of high-resource languages. |
| Approach: | They fine-tuned large language models (4B–671B parameters) on Arabic and evaluated zero-shot reading comprehension on Semitic languages and non-Semitic controls. |
| Outcome: | The results show that models with weak baselines improve across all languages, whereas strong-baseline models show only marginal gains regardless of language family. |
Self-Training Pre-Trained Language Models for Zero- and Few-Shot Multi-Dialectal Arabic Sequence Labeling (2021.eacl-main)
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| Challenge: | Existing approaches to fine-tune pre-trained language models for downstream tasks require labeled data. |
| Approach: | They propose to self-train pre-trained language models to improve performance on data-scarce varieties by as large as 10% F1 and 2% accuracy. |
| Outcome: | The proposed model improves zero-shot MSA-to-DA transfer by as large as 10% F1 (NER) and 2% accuracy (POS tagging). |
Deep Active Learning for Morphophonological Processing (2023.acl-short)
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Seyed Morteza Mirbostani, Yasaman Boreshban, Salam Khalifa, SeyedAbolghasem Mirroshandel, Owen Rambow
| Challenge: | Existing deep learning models for morphological processing require a large amount of annotated data. |
| Approach: | They propose a deep active learning method that uses only informative samples to reduce the need for annotated data. |
| Outcome: | The proposed method achieves the same results as the state-of-the-art model on Egyptian Arabic with only about 30% of annotated data. |
A Tale of Two Scripts: Transliteration and Post-Correction for Judeo-Arabic (2026.eacl-long)
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| Challenge: | Thousands of JA texts are available online, covering genres such as philosophy, biblical commentary, and Bible translations. |
| Approach: | They propose a two-step approach to automatically transliterate Judeo-Arabic into Arabic script using simple character-level mapping followed by post-correction to address grammatical and orthographic errors. |
| Outcome: | The proposed method enables Arabic NLP tools to perform morphosyntactic tagging and machine translation, which would have been impossible on the original texts. |
Dolphin: A Challenging and Diverse Benchmark for Arabic NLG (2023.findings-emnlp)
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| Challenge: | Existing benchmarks for Arabic are limited, but they can be used to measure performance of different languages. |
| Approach: | They propose a benchmark for Arabic that addresses the need for a framework dedicated to Arabic languages and varieties. |
| Outcome: | The proposed benchmark covers 13 different tasks in Arabic and spans 50 test splits. |
Learning to Generate Wikipedia Summaries for Underserved Languages from Wikidata (N18-2)
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Lucie-Aimée Kaffee, Hady Elsahar, Pavlos Vougiouklis, Christophe Gravier, Frédérique Laforest, Jonathon Hare, Elena Simperl
| Challenge: | Existing Wikipedia content is unevenly distributed among 287 languages . authors propose a neural network architecture that generates textual summaries from Wikidata triples . |
| Approach: | They propose an automated approach to generate Wikipedia summaries from Wikidata triples using structured data. |
| Outcome: | The proposed approach is tested on Arabic and Esperanto languages with limited editors and content in the most under-resourced Wikipedias. |
Multilingual Multi-class Sentiment Classification Using Convolutional Neural Networks (L18-1)
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| Challenge: | a new language-independent model for sentiment analysis is proposed for social media . a sentiment dictionary cannot list all the possible ways people can express their opinions . |
| Approach: | They propose a language-independent model for multi-class sentiment analysis using a neural network architecture. |
| Outcome: | The proposed model does not rely on language-specific features such as ontologies, dictionaries, or morphological or syntactic pre-processing. |
Code-Switching and Back-Transliteration Using a Bilingual Model (2024.findings-eacl)
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| Challenge: | a wealth of Judeo-Arabic texts is available on the internet, but most speakers of Arabic are unfamiliar with the Hebrew script. |
| Approach: | They propose two machine-learning models for transliterating Judeo-Arabic into Arabic . they also propose a model for identifying non-Arabian words, predominantly Hebrew and Aramaic . |
| Outcome: | The proposed models outperform previous solutions for transliterating Judeo-Arabic into Arabic and identifying non-Arabian words, mainly Hebrew and Aramaic. |
Arap-Tweet: A Large Multi-Dialect Twitter Corpus for Gender, Age and Language Variety Identification (L18-1)
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| Challenge: | Existing corpus of Arabic textual data is limited to English or other European languages. |
| Approach: | They present a large-scale and multi-dialectal corpus of Tweets from 11 regions and 16 countries in the arab world representing the major Arabic dialectal varieties. |
| Outcome: | The provided corpus will enrich the limited set of available language resources for Arabic and be invaluable enabler for developing author profiling tools and NLP tools for Arabic. |
Fine-Grained Arabic Dialect Identification (C18-1)
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| Challenge: | Existing work on Arabic Dialect Identification typically targeted coarse-grained five dialect classes plus Standard Arabic at most (6-way classification). |
| Approach: | They propose to tackle a fine-grained Arabic dialect classification task covering 25 cities from across the Arab World, in addition to Standard Arabic. |
| Outcome: | The proposed task can identify the exact city of a speaker at an accuracy of 67.9% for sentences with an average length of 7 words and reach more than 90% when we consider 16 words. |
On Using Arabic Language Dialects in Recommendation Systems (2025.findings-naacl)
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| Challenge: | Using natural language processing (NLP) to analyze user reviews in recommendation systems is unexplored. |
| Approach: | They propose to integrate Arabic dialects as a signal in recommendation systems by using explicit and implicit approaches. |
| Outcome: | The proposed approach improves recommendation performance and encourages further research in the Arab multicultural world. |
“Wikily” Supervised Neural Translation Tailored to Cross-Lingual Tasks (2021.emnlp-main)
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| Challenge: | Unsupervised neural machine translation models perform well in low-resource or distant languages. |
| Approach: | They propose a model that leverages Wikipedia for machine translation and cross-lingual tasks without supervision from external parallel data or supervised models in target language. |
| Outcome: | The proposed model outperforms supervised models in Arabic and English translation tasks. |
Arabic Diacritization Using Morphologically Informed Character-Level Model (2024.lrec-main)
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Muhammad Morsy Elmallah, Mahmoud Reda, Kareem Darwish, Abdelrahman El-Sheikh, Ashraf Hatim Elneima, Murtadha Aljubran, Nouf Alsaeed, Reem Mohammed, Mohamed Al-Badrashiny
| Challenge: | Diacritics are typically omitted in Arabic writings and the reader needs to guess the proper diacritics as they are reading. |
| Approach: | They propose a morphologically informed character-level model that can recover both types of diacritics simultaneously. |
| Outcome: | The proposed model achieves lowest word-level diacritization error rate for Classical Arabic, MSA, and two dialectal Arabic texts. |
An Automatic Learning of an Algerian Dialect Lexicon by using Multilingual Word Embeddings (L18-1)
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| Challenge: | a study on the Algerian Arabic dialect aims to build a lexicon of words written in Arabic or Latin script . multilinguality of the corpus is due to the fact that people use several languages to post comments . stretched letters, misspelled words, emoticons, condensed writing are among the problems . |
| Approach: | They propose to build automatically from a social network an Algerian dialect lexicon. |
| Outcome: | The proposed method leads to a score of 73% on a test lexicon . the study is based on analyzing a lexical corpus of an Algerian dialect . |
Morphosyntactic Tagging with Pre-trained Language Models for Arabic and its Dialects (2022.findings-acl)
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| Challenge: | Pre-trained morphosyntactic tagging models outperform existing systems in Modern Standard Arabic and all the Arabic dialects studied. |
| Approach: | They present results on morphosyntactic tagging across different varieties of Arabic using pre-trained transformer language models. |
| Outcome: | The proposed models outperform existing systems in Modern Standard Arabic, 2.8% in Gulf, 1.6% in Egyptian, and 8.3% in Levantine. |
Stanceosaurus: Classifying Stance Towards Multicultural Misinformation (2022.emnlp-main)
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| Challenge: | Existing corpora focus on misinformation spreading within western countries. |
| Approach: | They present a new corpus of tweets annotated with stance towards 250 misinformation claims. |
| Outcome: | The proposed method achieves 53.1 F1 on Hindi and 50.4 F1 in Arabic without any target-language fine-tuning. |
SemRel2024: A Collection of Semantic Textual Relatedness Datasets for 13 Languages (2024.findings-acl)
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Nedjma Ousidhoum, Shamsuddeen Muhammad, Mohamed Abdalla, Idris Abdulmumin, Ibrahim Ahmad, Sanchit Ahuja, Alham Aji, Vladimir Araujo, Abinew Ayele, Pavan Baswani, Meriem Beloucif, Chris Biemann, Sofia Bourhim, Christine Kock, Genet Dekebo, Oumaima Hourrane, Gopichand Kanumolu, Lokesh Madasu, Samuel Rutunda, Manish Shrivastava, Thamar Solorio, Nirmal Surange, Hailegnaw Tilaye, Krishnapriya Vishnubhotla, Genta Winata, Seid Yimam, Saif Mohammad
| Challenge: | SemRel datasets are annotated by native speakers across 13 languages . they are used to characterise the relationship between two units of text . |
| Approach: | They propose to use a semantic relatedness dataset to measure the degree of semantic textual relatedness between sentences in Afrikaans, Algerian Arabic, Amharic, English, Hausa, Hindi, Indonesian, Kinyarwanda, Marathi, Moroccan Arabic, Modern Standard Arabic, Spanish, and Telugu. |
| Outcome: | The proposed datasets are annotated by native speakers across 13 languages and represent the semantic relatedness of 13 languages. |
Revisiting Common Assumptions about Arabic Dialects in NLP (2025.acl-long)
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| Challenge: | Existing assumptions about Arabic dialect variation are not quantitatively verified. |
| Approach: | They extend and analyze Arabic dialects to assess their validity using a multi-label dataset . they find that the assumptions oversimplify reality and are not always accurate . |
| Outcome: | The proposed methods oversimplify reality and are not always accurate, the authors argue . they show that the proposed assumptions oversimply represent reality and may hinder future work . |
FloDusTA: Saudi Tweets Dataset for Flood, Dust Storm, and Traffic Accident Events (2020.lrec-1)
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| Challenge: | Detecting events from tweets can help to predict real-world events precisely. |
| Approach: | They propose to use tweets written in Arabic and Saudi dialects to identify events from tweets. |
| Outcome: | The proposed system can detect flood, dust storm, traffic accident, and non-event. |
Build Fast and Accurate Lemmatization for Arabic (L18-1)
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| Challenge: | Lemmatization is the process of finding the base form (lemma) of a word by considering its inflected forms. |
| Approach: | They propose a lemmatizer for Arabic with a dataset that can be used to test lemma accuracy. |
| Outcome: | The proposed algorithm outperforms state-of-the-art Arabic lemmatization in accuracy and speed. |
SentiArabic: A Sentiment Analyzer for Standard Arabic (L18-1)
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| Challenge: | Sentiment analysis is a process of applying computational approaches to identify attitudes, emotions and opinions in text, speech and visual data. |
| Approach: | They propose a sentiment analyzer that identifies the overall contextual polarity for Arabic text. |
| Outcome: | The proposed system achieves an F-score of 76.5% when evaluated on a blind test set. |
Annotating Verbal Multiword Expressions in Arabic: Assessing the Validity of a Multilingual Annotation Procedure (2022.lrec-1)
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Najet Hadj Mohamed, Cherifa Ben Khelil, Agata Savary, Iskandar Keskes, Jean-Yves Antoine, Lamia Hadrich-Belguith
| Challenge: | a subset of 1,062 sentences from the Prague Arabic Dependency Treebank PADT were selected and annotated by two Arabic native speakers independently. |
| Approach: | They propose to use Arabic as an annotation framework to extend PARSEME to modern standard Arabic by measuring inter-annotator agreement. |
| Outcome: | The proposed framework is based on a subset of 1,062 sentences from the Prague Arabic Dependency Treebank PADT and is already exceeding the smallest corpus of the PARSEME suite. |
The Arabic Parallel Gender Corpus 2.0: Extensions and Analyses (2022.lrec-1)
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| Challenge: | Gender bias in natural language processing (NLP) applications has been receiving increasing attention, largely due to the lack of datasets and resources. |
| Approach: | They propose a corpus for gender identification and rewriting in contexts involving one or two target users with independent grammatical gender preferences. |
| Outcome: | The proposed corpus expands on Habash et al.'s Arabic Parallel Gender Corpus (APGC) by adding second person targets and increasing the total number of sentences over 6.5 times, reaching over 590K words. |
A Robust Abstractive System for Cross-Lingual Summarization (N19-1)
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| Challenge: | We present a novel system for cross-lingual summarization that can be applied to low-resource languages. |
| Approach: | They propose a neural abstractive summarization system that can be applied to low-resource languages . they use machine translation and the New York Times summarizing corpus to create a corpus . |
| Outcome: | The proposed system achieves higher fluency than standard summarizers on translated documents . the proposed system can be easily applied to new low-resource languages . |
Multitask Easy-First Dependency Parsing: Exploiting Complementarities of Different Dependency Representations (2020.coling-main)
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| Challenge: | Existing dependency parsing models for Arabic use complementary annotations, CATiB and UD treebanks, and partially created trees for one annotation are also available to the other as features for the score function. |
| Approach: | They propose to use Arabic dependency annotations to parse projective dependency trees using CATiB and UD treebanks. |
| Outcome: | The proposed model gives 9.9% error reduction on CATiB and 6.1% on UD compared to a strong baseline and ablation tests show that the main contribution is given by sharing tree representation between tasks, and not simply sharing biLSTM layers as is often performed in NLP multitask systems. |
Once Correct, Still Wrong: Counterfactual Hallucination in Multilingual Vision-Language Models (2026.findings-acl)
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| Challenge: | Existing hallucination benchmarks rarely test this failure mode outside Western contexts and English. |
| Approach: | They propose a multimodal benchmark built from images spanning 17 MENA countries . they use a CFHR-based test to measure hallucination beyond raw accuracy . |
| Outcome: | The proposed model is based on images from 17 MENA countries . it measures counterfactual acceptance conditioned on correctly answering the true statement. |
Improving English-Arabic Transliteration with Phonemic Memories (2022.findings-emnlp)
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| Challenge: | Existing neural approaches to transliterate names from English to Arabic are limited and focus on leveraging the phonemic association between English and Arabic. |
| Approach: | They propose a model for English-Arabic transliteration using a memory module modeling the phonemic association between English and Arabic to guide the transliterations process. |
| Outcome: | The proposed model improves on EANames corpus, which better represents names in the general public than linked Wikipedia entries that are always names of famous people. |
Camel Morph MSA: A Large-Scale Open-Source Morphological Analyzer for Modern Standard Arabic (2024.lrec-main)
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| Challenge: | Camel Morph MSA is the largest open-source Modern Standard Arabic morphological analyzer and generator. |
| Approach: | They present Camel Morph MSA, the largest open-source Arabic morphological analyzer and generator. |
| Outcome: | The analysis can produce 1.45B analyses and 535M unique diacritizations, almost an order of magnitude larger than SAMA on a 10B word corpus. |
Cross-lingual Machine Reading Comprehension with Language Branch Knowledge Distillation (2020.coling-main)
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| Challenge: | Cross-lingual Machine Reading Comprehension (CLMRC) is a challenging problem due to the lack of large-scale annotated datasets in low-source languages, such as Arabic, Hindi, and Vietnamese. |
| Approach: | They propose a novel approach to augment cross-lingual machine reading comprehension by combining knowledge from multiple language branch models into a single model for all target languages. |
| Outcome: | Extensive experiments on two CLMRC benchmarks show the proposed method is effective and robust to data noises. |
The Bahrain Corpus: A Multi-genre Corpus of Bahraini Arabic (2022.lrec-1)
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| Challenge: | Various corpora of various sizes and representing different genres, have been created for various Arabic dialects. |
| Approach: | They propose to create a specialized corpus of Bahraini Arabic dialect, which includes written texts as well as transcripts of audio files. |
| Outcome: | The proposed corpus includes 620K words representing the Bahraini Arabic dialect . the annotated corpus is available to support researchers interested in Arabic NLP . |
Evaluating Emotion Arcs Across Languages: Bridging the Global Divide in Sentiment Analysis (2023.findings-emnlp)
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| Challenge: | Emotion arcs capture how an individual (or a population) feels over time. |
| Approach: | They compare machine-learning and Lexicon-Only methods to generate emotion arcs . they run experiments on 18 diverse datasets in 9 languages . |
| Outcome: | The proposed method is poor at instance level emotion classification, but highly accurate when aggregating information from hundreds of instances. |
Advocating Character Error Rate for Multilingual ASR Evaluation (2025.findings-naacl)
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| Challenge: | Word error rate (WER) has been used for automatic speech recognition (ASR) evaluations for English datasets for many years. |
| Approach: | They propose to use the character error rate as the primary metric in multilingual ASR evaluation to account for morphologically complex languages. |
| Outcome: | The character error rate (CER) is the primary evaluation metric in multilingual ASR evaluation. |
AraDiCE: Benchmarks for Dialectal and Cultural Capabilities in LLMs (2025.coling-main)
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Basel Mousi, Nadir Durrani, Fatema Ahmad, Md. Arid Hasan, Maram Hasanain, Tameem Kabbani, Fahim Dalvi, Shammur Absar Chowdhury, Firoj Alam
| Challenge: | a recent study has found that Arabic is underrepresented in Large Language Models, especially in dialectal variations. |
| Approach: | They propose a benchmark for Arabic Dialect and Cultural Evaluation that evaluates Arabic dialect comprehension and generation. |
| Outcome: | The proposed model outperforms multilingual models on dialect comprehension and generation, but significant challenges persist in dialect identification, generation, and translation. |
Arabic Dataset for LLM Safeguard Evaluation (2025.naacl-long)
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| Challenge: | Existing studies on large language models have focused on English, but the safety of LLMs in Arabic remains under-explored. |
| Approach: | They propose to use Arabic-region-specific questions to evaluate LLMs' safety . they use a dual-perspective evaluation framework to examine differences between LLM responses . |
| Outcome: | The proposed framework assesses the LLM responses from both governmental and opposition viewpoints. |
Nahw: A Comprehensive Benchmark of Arabic Grammar Understanding, Error Detection, Correction, and Explanation (2026.eacl-long)
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| Challenge: | Existing corpora address individual linguistic aspects like spelling or diacritization, but rarely provide explanations of grammatical errors. Existing datasets and benchmarks that capture Arabic's grammatological complexity are scarce. |
| Approach: | They propose a benchmark for Arabic grammar that covers error detection, correction, and explanation. |
| Outcome: | The proposed model performs better on GPT-4o than on the best performing model (ALLaM-7B) despite fine tuning with synthetic data, the model perform better on Arabic grammar tasks. |
A Survey of Code-switched Arabic NLP: Progress, Challenges, and Future Directions (2025.coling-main)
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| Challenge: | Code-switching (CSW) is a common linguistic phenomenon in multilingual societies . current literature on CSW in the arab world is limited to the Arabic language . |
| Approach: | They present a review of the literature in the field of code-switched Arabic NLP . they propose recommendations for future research . |
| Outcome: | This review provides a broad perspective on the current literature in the field of code-switched Arabic NLP . it also provides recommendations for future research . |
The interplay between language similarity and script on a novel multi-layer Algerian dialect corpus (2021.findings-acl)
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| Challenge: | Recent studies have focused on cross-lingual transfer between languages with similar typology and languages of different scripts. |
| Approach: | They propose to annotate Algerian user-generated comments with parallel annotations . they also investigate the effect of script vs. language similarity in cross-lingual transfer . |
| Outcome: | The proposed model fine-tunes multi-lingual models on Algerian language and scripts . it shows that script vs. language similarity is important for part-of-speech tagging and sentiment analysis . |
On The Origin of Cultural Biases in Language Models: From Pre-training Data to Linguistic Phenomena (2025.naacl-long)
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| Challenge: | Language Models (LMs) have been shown to exhibit a strong preference towards entities associated with Western culture when operating in non-Western languages. |
| Approach: | They propose a parallel Arabic-English benchmark of 58,086 entities associated with Arab and Western cultures and 367 masked natural contexts for entities. |
| Outcome: | The proposed model shows that LMs struggle in Arabic with entities that appear at high frequencies in pre-training, where entities can hold multiple word senses. |
ArabicMMLU: Assessing Massive Multitask Language Understanding in Arabic (2024.findings-acl)
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Fajri Koto, Haonan Li, Sara Shatnawi, Jad Doughman, Abdelrahman Sadallah, Aisha Alraeesi, Khalid Almubarak, Zaid Alyafeai, Neha Sengupta, Shady Shehata, Nizar Habash, Preslav Nakov, Timothy Baldwin
| Challenge: | evaluating language models in Arabic remains challenging due to limited datasets . focus has shift to reasoning and knowledge-intensive tasks due to lack of relevant datasets. |
| Approach: | They propose to use ArabicMMLU to evaluate models' understanding of Arabic . they use 40 tasks and 14,575 multiple-choice questions from school exams in different countries . |
| Outcome: | The ArabicMMLU is the first multi-task language understanding benchmark for the Arabic language . it is based on 40 tasks and 14,575 multiple-choice questions in modern standard Arabic . the models are based in different countries across North Africa, the Levant, and the Gulf regions . |
Cards Against Contamination: TCG-Bench for Difficulty-Scalable Multilingual LLM Reasoning (2026.findings-eacl)
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| Challenge: | Recent studies find 25-50% of evaluation datasets appear in training corpora . contamination hinders the possibility to differentiate memorization and reasoning skills. |
| Approach: | They propose a two-player trading card game that is contaminated by a public engine and hidden card implementations to prevent benchmark saturation. |
| Outcome: | The proposed benchmark is based on a new two-player trading card game similar to Magic: The Gathering. |
A Leveled Reading Corpus of Modern Standard Arabic (L18-1)
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| Challenge: | Using a reading corpus in Modern Standard Arabic, we explore the lexical coverage of textbooks and unabridged works of fiction. |
| Approach: | They propose to use textbooks from the United Arab Emirates curriculum and a reading corpus in Modern Standard Arabic to enrich the sparse collection of resources available for educational applications. |
| Outcome: | The corpus spans all 12 grades and contains 129 unabridged works of fiction spanning grades 1-12 . lexical coverage is compared to other genres, and the results show that the two sub-corpora are similar to each other to measure their genres. |
A Large-Scale Leveled Readability Lexicon for Standard Arabic (2020.lrec-1)
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| Challenge: | a large-scale leveled readability lexicon for Modern Standard Arabic is not available in many other languages. |
| Approach: | They propose a large-scale leveled readability lexicon for Arabic with 26,000 lemmas . they manually annotate a lexico from three different regions in the arab world . |
| Outcome: | The proposed lexicon is publicly available for Arabic readability tasks. |
Commonsense Reasoning in Arab Culture (2025.acl-long)
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Abdelrahman Sadallah, Junior Cedric Tonga, Khalid Almubarak, Saeed Almheiri, Farah Atif, Chatrine Qwaider, Karima Kadaoui, Sara Shatnawi, Yaser Alesh, Fajri Koto
| Challenge: | Existing studies on commonsense reasoning in Arabic have relied on machine translations that lack cultural depth and introduce anglocentric biases. |
| Approach: | They propose a commonsense reasoning dataset in Arabic that covers 13 Arab countries. |
| Outcome: | The proposed dataset covers 13 countries across the Gulf, Levant, North Africa, and the Nile Valley. |
A Pointer Network Architecture for Joint Morphological Segmentation and Tagging (2020.findings-emnlp)
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| Challenge: | Morphological Disambiguation (MD) is a task of decomposing tokens into morphemes . a simple pipeline is used to segment and tagging raw tokens . |
| Approach: | They propose a new pointer network model that combines symbolic knowledge of morphemes with the learning capacity of neural end-to-end modeling. |
| Outcome: | The proposed model outperforms all previous reported results on Hebrew and Turkish . it uses morphological knowledge and the learning capacity of neural end-to-end modeling . |
STTATTS: Unified Speech-To-Text And Text-To-Speech Model (2024.findings-emnlp)
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| Challenge: | a multi-task learning approach is currently available for speech recognition and speech synthesis models . |
| Approach: | They propose a parameter-efficient approach to learning ASR and TTS jointly . they use English as a resource-rich language and Arabic as 'low-resource' language . |
| Outcome: | The proposed model saves 50% of computational and memory costs while learning ASR and TTS jointly. |
The Morpho-syntactic Annotation of Animacy for a Dependency Parser (L18-1)
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| Challenge: | Animacy is a feature found in nouns such as 'gender', 'number' and 'case' that improves parser accuracy. |
| Approach: | They propose an annotation scheme and parser results for the animacy feature in Russian and Arabic, morphologically rich languages, using the universal dependency framework. |
| Outcome: | The proposed scheme and parser improve on the animacy feature in Russian and Arabic, and the results show that the feature is more accurate than other features found in nouns, namely, 'gender', , and 'number' |
Candidate-Aware Retrieval and Reranking for Multiple-Choice Question Answering: Arabic as a Case Study (2026.findings-acl)
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| Challenge: | Large language models (LLMs) have recently achieved impressive results on multiple-choice question answering (MCQA) despite advances in English, LLMs continue to underperform in Arabic due to gaps in data coverage, linguistic transfer, and evaluation design. |
| Approach: | They propose a method that jointly models the relevance of both the question and its candidate answers when selecting contextual passages. |
| Outcome: | The proposed approach outperforms standard RAG baselines and reranker baselines while remaining competitive with considerably larger models. |
AraBench: Benchmarking Dialectal Arabic-English Machine Translation (2020.coling-main)
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| Challenge: | Existing efforts to translate Arabic dialects to English are limited due to the lack of evaluation benchmarks. |
| Approach: | They propose an evaluation suite for Arabic to English machine translation using existing Arabic resources. |
| Outcome: | The evaluation suite for Arabic to English machine translation is based on existing evaluation benchmarks. |
AceGPT, Localizing Large Language Models in Arabic (2024.naacl-long)
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Huang Huang, Fei Yu, Jianqing Zhu, Xuening Sun, Hao Cheng, Song Dingjie, Zhihong Chen, Mosen Alharthi, Bang An, Juncai He, Ziche Liu, Junying Chen, Jianquan Li, Benyou Wang, Lian Zhang, Ruoyu Sun, Xiang Wan, Haizhou Li, Jinchao Xu
| Challenge: | Significant concerns emerge when addressing cultural sensitivity and local values. |
| Approach: | They propose a localized Large Language Model (LLM) specifically for Arabic, a language imbued with unique cultural characteristics inadequately addressed by current mainstream models. |
| Outcome: | The proposed model sets the state-of-the-art standard for open Arabic LLMs across various benchmarks. |
MILIE: Modular & Iterative Multilingual Open Information Extraction (2022.acl-long)
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Bhushan Kotnis, Kiril Gashteovski, Daniel Rubio, Ammar Shaker, Vanesa Rodriguez-Tembras, Makoto Takamoto, Mathias Niepert, Carolin Lawrence
| Challenge: | Current OpenIE systems extract all triple slots independently. |
| Approach: | They propose a neural OpenIE system that extracts triple slots iteratively . they propose to use the system to extract easy slots and difficult ones . |
| Outcome: | The proposed system outperforms SOTA systems on multiple languages ranging from Chinese to Arabic. |
Morphological Analysis and Disambiguation for Gulf Arabic: The Interplay between Resources and Methods (2020.lrec-1)
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| Challenge: | Despite advances in the field of natural language processing, many dialectal Arabic varieties are lagging behind . despite advances in NLP, many Arabic dialects are considered under-resourced . |
| Approach: | They propose a full morphological analysis and disambiguation system for Gulf Arabic . they use existing state-of-the-art morphology tools to investigate the effects of different data sizes and combinations of morphologists. |
| Outcome: | The proposed system improves on the existing system for Arabic . it is based on a set of data sizes and combinations of morphological analyzers . |
Hierarchical Aggregation of Dialectal Data for Arabic Dialect Identification (2022.lrec-1)
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| Challenge: | Previous work on Arabic Dialect identification focused on specific dialect levels and labels . since dialectal differences tend to be more subtle relative terms to language differences, the DID task is harder than language identification. |
| Approach: | They propose to define a standard hierarchical schema for Arabic Dialect identification . they map 29 different data sets to this schema and use it to aggregate the data . |
| Outcome: | The proposed schemas and methods are extensible to other languages and dialect groups. |
EMAD: A Bridge Tagset for Unifying Arabic POS Annotations (2024.lrec-main)
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| Challenge: | Existing tagsets for Arabic are difficult to combine due to the diversity of their features. |
| Approach: | They propose an Arabic Extended Morphological Analysis and Disambiguation Tagset which facilitates conversion and unification of Arabic tagsets. |
| Outcome: | The proposed tagset facilitates conversion and unification of different tagsetes used to annotate Arabic corpora. |
A Spelling Correction Corpus for Multiple Arabic Dialects (2020.lrec-1)
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| Challenge: | Arabic dialects are non-standard varieties of Arabic commonly spoken across the Arab world, but lack standard orthographies. |
| Approach: | They present a corpus of 10,000 sentences from five Arabic city dialects represented in the Conventional Orthography for Dialectal Arabic (CODA) they use a bootstrapping technique to speed up annotation and compare similarity between dialects before and after CODA annotation. |
| Outcome: | The proposed method speeds up the annotation process and shows similarity between the dialects before and after CODA annotation. |
Leveraging Large Pre-trained Multilingual Models for High-Quality Speech-to-Text Translation on Industry Scenarios (2025.coling-main)
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| Challenge: | Speech-to-Text Translation systems rely on a sequential pipeline that combines ASR and MT models. |
| Approach: | They propose a parameter-efficient framework that integrates one LPSM with a multilingual MT engine. |
| Outcome: | The proposed framework integrates one LPSM with a multilingual MT engine. |
Enhancing Deep Learning with Embedded Features for Arabic Named Entity Recognition (2022.lrec-1)
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| Challenge: | Word embeddings can capture the semantics of words and other hidden features, but the Arabic language is complex and requires a large amount of information to process. |
| Approach: | They propose to add morphological and syntactical features to Arabic word embeddings to train the model. |
| Outcome: | The proposed model outperforms the previous systems to the best of our knowledge. |
The SADID Evaluation Datasets for Low-Resource Spoken Language Machine Translation of Arabic Dialects (2020.coling-main)
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| Challenge: | Low-resource Machine Translation (LRT) models are still lagging behind on low-resourced language pairs due to the scarcity of parallel training data. |
| Approach: | They introduce benchmark datasets for Arabic and its dialects to examine their properties . they bootstrap existing parallel sentences and complement this with multilingual training . |
| Outcome: | The proposed method bootstraps existing parallel sentences and complements multilingual training to achieve strong baselines. |
LemmaTag: Jointly Tagging and Lemmatizing for Morphologically Rich Languages with BRNNs (D18-1)
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| Challenge: | We compare morphologically rich languages with analytical languages like English due to the large vocabulary size and data sparsity. |
| Approach: | They propose a featureless neural network architecture that generates part-of-speech tags and lemmas for sentences by using bidirectional RNNs with character-level and word-level embeddings. |
| Outcome: | The proposed model outperforms state-of-the-art models in Czech, German, and Arabic. |
RoBERT2VecTM: A Novel Approach for Topic Extraction in Islamic Studies (2024.findings-emnlp)
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| Challenge: | a new approach to investigate “Hadith” texts presents challenges due to the complexity of Arabic . a novel neural-based approach to analyze “Matn” topics outperforms traditional NLP models . |
| Approach: | They propose a novel approach to analyze Arabic “Hadith” texts using the Contextualized Topic Model. |
| Outcome: | The proposed approach outperforms state-of-the-art models by generating more coherent topics in Arabic. |
Arabic Dialect Identification in the Context of Bivalency and Code-Switching (L18-1)
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| Challenge: | Existing methods for identifying Arabic dialects require significant amounts of annotated training data which is costly and time consuming to produce. |
| Approach: | They propose a novel approach to Arabic dialect identification using language bivalency and written code-switching to identify Arabic dialects. |
| Outcome: | The proposed method can reach more than 76% and score well (66%) when tested on unseen data. |
Unified Guidelines and Resources for Arabic Dialect Orthography (L18-1)
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Nizar Habash, Fadhl Eryani, Salam Khalifa, Owen Rambow, Dana Abdulrahim, Alexander Erdmann, Reem Faraj, Wajdi Zaghouani, Houda Bouamor, Nasser Zalmout, Sara Hassan, Faisal Al-Shargi, Sakhar Alkhereyf, Basma Abdulkareem, Ramy Eskander, Mohammad Salameh, Hind Saddiki
| Challenge: | Existing efforts to conventionalize the dialectal orthography of Arabic have focused on specific dialects and made ad hoc decisions. |
| Approach: | They propose a set of guidelines and meta-guidelines for conventional orthography of Arabic dialects . they apply them to 28 Arab city dialects from Rabat to Muscat . |
| Outcome: | The proposed guidelines and resources are being used by three large Arabic dialect processing projects in three universities. |
Automatic Identification of Maghreb Dialects Using a Dictionary-Based Approach (L18-1)
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| Challenge: | Automatic identification of Arabic dialects in texts is difficult, especially for Maghreb languages and when they are written in Arabic or Latin characters (Arabizi). |
| Approach: | They propose a dictionary-based approach to detect Arabic dialects in texts . they focus on transliteration of Arabicizi into Latin script and code-switching . |
| Outcome: | The proposed approach shows that it is possible to detect dialects in Arabic and Latin scripts. |
You Tweet What You Speak: A City-Level Dataset of Arabic Dialects (L18-1)
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| Challenge: | Existing studies of Arabic dialects have focused on blogs and comments on online news sites, but data on other dialects are costly and limited. |
| Approach: | They present a dataset of > 1/4 billion tweets representing a wide range of Arabic dialects. |
| Outcome: | The dataset represents 29 major Arab cities from 10 Arab countries with varying dialects. |
Beyond Dataset Creation: Critical View of Annotation Variation and Bias Probing of a Dataset for Online Radical Content Detection (2025.coling-main)
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| Challenge: | Existing datasets and models fail to address the complexities of multilingual data, authors say . detection of radical content on online platforms has become an increasingly pressing concern . |
| Approach: | They propose a publicly available multilingual dataset annotated with radicalization levels, calls for action, and named entities in English, French, and Arabic. |
| Outcome: | The proposed dataset is annotated with radicalization levels, calls for action, and named entities in English, French, and Arabic. |
AraTrust: An Evaluation of Trustworthiness for LLMs in Arabic (2025.coling-main)
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| Challenge: | Existing benchmarks for large language models (LLMs) in Arabic are lacking . despite progress in their development, there is a lack of comprehensive trustworthiness evaluation benchmarks . |
| Approach: | They propose to use Arabic as a language to assess trustworthiness of large language models. |
| Outcome: | The proposed benchmark measures the trustworthiness of large language models in Arabic. |
Classification of Closely Related Sub-dialects of Arabic Using Support-Vector Machines (L18-1)
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| Challenge: | Existing studies on dialect identification have focused on binary classifications between colloquial Arabic and dialectal Egyptian . |
| Approach: | They propose to use an n-gram based SVM to classify on a fine-grained sub-dialectal level and compare it to methods used in dialect classification such as vocabulary pruning. |
| Outcome: | The proposed method is compared to methods used in dialect classification such as vocabulary pruning of shared items across dialects. |
Developing an Arabic Infectious Disease Ontology to Include Non-Standard Terminology (2020.lrec-1)
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| Challenge: | Existing ontologies for Arabic are difficult due to the lack of resources and the nature of the Arabic language. |
| Approach: | They propose to build an Arabic ontology that integrates scientific vocabularies with informal equivalents. |
| Outcome: | The proposed ontology integrates scientific vocabularies with informal equivalents in Arabic . it will be automatically generated but the results will be evaluated by a domain expert . |
ORCA: A Challenging Benchmark for Arabic Language Understanding (2023.findings-acl)
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| Challenge: | Despite efforts to evaluate Arabic NLU, no public benchmark of diverse nature exists . a benchmark targeting Arabic needs to take into account that Arabic is not a single language but a collection of languages and language varieties. |
| Approach: | They propose a publicly available benchmark for Arabic language understanding evaluation dubbed ORCA . it covers diverse Arabic varieties and a wide range of Arabic understanding tasks . |
| Outcome: | The proposed benchmark covers Arabic and multilingual models across seven NLU task clusters. |
Evaluating Inflectional Complexity Crosslinguistically: a Processing Perspective (L18-1)
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| Challenge: | a cognitively motivated method for evaluating the inflectional complexity of a language is proposed . authors argue that some languages are inflectionally more complex than others . |
| Approach: | They propose a cognitively motivated method for evaluating inflectional complexity of a language . they use a recurrent self-organising neural network to learn "raw" inflected word forms . |
| Outcome: | The proposed method is independent of meta-linguistic issues and language-specific typological aspects. |
Multi-lingual Argumentative Corpora in English, Turkish, Greek, Albanian, Croatian, Serbian, Macedonian, Bulgarian, Romanian and Arabic (L18-1)
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Alfred Sliwa, Yuan Ma, Ruishen Liu, Niravkumar Borad, Seyedeh Ziyaei, Mina Ghobadi, Firas Sabbah, Ahmet Aker
| Challenge: | Argumentative corpora are costly to create and available only in few languages with English dominating the area. |
| Approach: | They use 8 different argument mining classifiers trained for English to build a parallel corpora in which the source language is English and the target language is either a Balkan language or Arabic. |
| Outcome: | The proposed method is based on 8 different argument mining classifiers trained for English and project the decision to the target language. |
Part-of-Speech Tagging for Arabic Gulf Dialect Using Bi-LSTM (L18-1)
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| Challenge: | Part-of-speech (POS) tagging is one of the most important building blocks in many natural language processing (NLP) applications. |
| Approach: | They propose to use a POS tagger for Arabic Gulf dialect to improve POS tagging accuracy. |
| Outcome: | The proposed POS tagger improves POS tagging accuracy for the Arabic Gulf dialect from 75% accuracy to 91% accuracy using a bi-LSTM labeler. |
Web-based Annotation Tool for Inflectional Language Resources (L18-1)
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| Challenge: | Wasim is a web-based tool for semi-automatic morphosyntactic annotation of inflectional languages. |
| Approach: | They present a web-based tool for semi-automatic morphosyntactic annotation of inflectional languages resources. |
| Outcome: | The tool has high flexibility in segmenting tokens, editing, diacritizing, labelling tokens and segments. |
Annotating Attribution Relations in Arabic (L18-1)
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| Challenge: | Current studies focus on using lexical terms in long texts to verify author identity. |
| Approach: | They propose to annotate attributed arguments to the source in Arabic news with required syntactical and semantic features with required features. |
| Outcome: | The proposed method is applied to Arabic news and is compared with existing tools and methods. |
ALDi: Quantifying the Arabic Level of Dialectness of Text (2023.emnlp-main)
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| Challenge: | Existing work on Dialect Identification (DI) on the sentence level has focused on binary tasks, whereas ALDi treats the task as binary. |
| Approach: | They propose a dataset which contains 127,835 sentences manually labeled with their level of dialectness. |
| Outcome: | The proposed model can identify dialectness on a range of other corpora, providing a more nuanced picture than traditional DI systems. |
MOLE: Metadata Extraction and Validation in Scientific Papers Using LLMs (2025.findings-emnlp)
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| Challenge: | Metadata extraction relies heavily on manual annotation of documents. |
| Approach: | They propose a framework that leverages Large Language Models to automatically extract metadata attributes from scientific papers covering datasets of languages other than Arabic. |
| Outcome: | The proposed framework automates the extraction of metadata attributes from Arabic scientific papers using large language models. |
Arabic Speech Rhythm Corpus: Read and Spontaneous Speaking Styles (2020.lrec-1)
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| Challenge: | a corpus of Arabic speech recordings has been built to allow comparisons between Arabic and other languages. |
| Approach: | They propose to build a corpus of Arabic speech recordings that can be compared with other languages. |
| Outcome: | The proposed corpus can be used for forensic phonetic research and casework applications. |
Investigating Cultural Alignment of Large Language Models (2024.acl-long)
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| Challenge: | Large Language Models (LLMs) are used to represent the diversity of human experience and culturally sensitive topics. |
| Approach: | They propose a method leveraging anthropological reasoning to enhance cultural alignment by prompting LLMs with different pretraining data mixtures in Arabic and English. |
| Outcome: | The proposed method enables users to better represent the diversity of human experience and the plurality of different cultures. |
To Distill or Not to Distill? On the Robustness of Robust Knowledge Distillation (2024.acl-long)
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| Challenge: | Existing models for multilingual automatic speech recognition (ASR) are computationallyintensive and lack proper comprehensive evaluations. |
| Approach: | They propose to distill knowledge from large teacher models into smaller student variants that are more efficient. |
| Outcome: | The proposed model outperforms existing models on standard benchmarks and dialectal data. |
Peacock: A Family of Arabic Multimodal Large Language Models and Benchmarks (2024.acl-long)
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Fakhraddin Alwajih, El Moatez Billah Nagoudi, Gagan Bhatia, Abdelrahman Mohamed, Muhammad Abdul-Mageed
| Challenge: | MLLMs have proven effective in a wide range of tasks that require complex reasoning and linguistic comprehension, but they are limited to English-based settings. |
| Approach: | They propose a family of Arabic multimodal large language models with strong vision and language capabilities. |
| Outcome: | The proposed models show strong performance on visual reasoning tasks and language capabilities. |
Halwasa: Quantify and Analyze Hallucinations in Large Language Models: Arabic as a Case Study (2024.lrec-main)
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| Challenge: | Large Language Models (LLMs) generate text that is factually incorrect, nonsensical, or misleading. |
| Approach: | They create a large Arabic dataset that contains 10K of LLM generated sentences and annotate it for factuality and correctness. |
| Outcome: | The proposed dataset analyzes 10K of generated sentences and finds 25% of them are factually incorrect. |
Beyond Orthography: Automatic Recovery of Short Vowels and Dialectal Sounds in Arabic (2024.acl-long)
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| Challenge: | Existing algorithms for recognizing borrowed and dialectal sounds are limited to Arabic, a dialect-rich language containing more than 22 major dialects. |
| Approach: | They propose a framework to recognize borrowed and dialectal sounds within phonologically diverse and dialect-rich languages that extends beyond its standard orthographic sound sets. |
| Outcome: | The proposed framework improves character error rate by 7% with only one and half hours of training data compared to the baseline. |
A Multitask Learning Approach for Diacritic Restoration (2020.acl-main)
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| Challenge: | Diacritics are used to specify pronunciations and meanings in many languages like Arabic. |
| Approach: | They propose to use multi-task learning to optimize diacritic restoration with related NLP problems . they use Arabic as a case study since it has sufficient data resources for tasks . |
| Outcome: | The proposed model outperforms baseline models and is comparable to the state-of-the-art models. |
Joint Diacritization, Lemmatization, Normalization, and Fine-Grained Morphological Tagging (2020.acl-main)
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| Challenge: | a word can have multiple interpretations and is one of many inflected forms of the same concept or lemma. |
| Approach: | They propose to model morphological features jointly, whether lexicalized or non-lexicalised . their results are compared to Arabic and Egyptian Arabic . |
| Outcome: | The proposed model achieves 20% relative error reduction in Arabic and 11% in Egyptian Arabic. |
HumVI: A Multilingual Dataset for Detecting Violent Incidents Impacting Humanitarian Aid (2024.findings-emnlp)
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Hemank Lamba, Anton Abilov, Ke Zhang, Elizabeth Olson, Henry Dambanemuya, João Bárcia, David Batista, Christina Wille, Aoife Cahill, Joel Tetreault, Alejandro Jaimes
| Challenge: | Humanitarian organizations can analyze data to discover trends, gather aggregated insights, manage security risks, and inform advocacy and funding proposals. |
| Approach: | They present a dataset comprising news articles in three languages containing instances of different types of violent incidents categorized by the humanitarian sector they impact. |
| Outcome: | The proposed framework can be used to identify violent incidents and identify their impact on humanitarian operations. |
Cross-lingual Emotion Detection (2022.lrec-1)
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| Challenge: | Emotion detection is a useful tool for understanding human behavior, but constructing annotated datasets to train models can be expensive. |
| Approach: | They propose to use English as the source language with Arabic and Spanish as target languages to train models for emotion detection in a target language. |
| Outcome: | The proposed approaches surpass state-of-the-art models in Arabic and Spanish by 4% and 5% respectively. |
CIDAR: Culturally Relevant Instruction Dataset For Arabic (2024.findings-acl)
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Zaid Alyafeai, Khalid Almubarak, Ahmed Ashraf, Deema Alnuhait, Saied Alshahrani, Gubran Abdulrahman, Gamil Ahmed, Qais Gawah, Zead Saleh, Mustafa Ghaleb, Yousef Ali, Maged Al-shaibani
| Challenge: | Instruction tuning datasets predominantly cater to English or are derived from English-dominated LLMs. |
| Approach: | They propose to use an Arabic instruction tuning dataset culturally aligned by native Arabic speakers to address drawbacks of finetuning LLMs on machine-generated or machinetranslated datasets. |
| Outcome: | The proposed datasets show that they achieve better cultural alignment than models fine-tuned on other datasets. |
DAICT: A Dialectal Arabic Irony Corpus Extracted from Twitter (2020.lrec-1)
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| Challenge: | Current scholarship is yet to reach an agreement on a universal definition of the concept of irony. |
| Approach: | They propose to query Twitter using irony-related hashtags to collect ironic messages which are then manually annotated by two linguists according to their working definition of irony. |
| Outcome: | The proposed corpus will be a valuable resource for developing open domain systems for automatic irony recognition in Arabic and its dialects in social media text. |
Advancing Arabic Diacritization: Improved Datasets, Benchmarking, and State-of-the-Art Models (2025.emnlp-main)
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| Challenge: | Arabic diacritics are typically omitted in written Arabic, leading to ambiguity . authors propose a methodology to analyze and refine a large diacritized corpus . |
| Approach: | They propose a methodology to analyze and refine a large diacritized corpus to improve training quality. |
| Outcome: | The proposed model achieves state-of-the-art results with 3.12% and 2.70% WER on WikiNews-2014 and Wikinews-2024. |
Enhancing Text Editing for Grammatical Error Correction: Arabic as a Case Study (2025.acl-long)
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| Challenge: | Text editing is a wellstudied problem for grammatical error correction (GEC) but it is not the most efficient for morphologically rich languages like Arabic. |
| Approach: | They propose a text editing approach that derives edit tags directly from data, eliminating the need for language-specific edits. |
| Outcome: | The proposed approach achieves SOTA results on Arabic and performs on par with SOTA on two other languages. |
IDRISI-RA: The First Arabic Location Mention Recognition Dataset of Disaster Tweets (2023.acl-long)
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| Challenge: | a low resource language such as Arabic is understudied for geolocation extraction . a recent study found that geolocation is underutilized for low resource languages such as arabic . |
| Approach: | They propose a publicly-available Arabic Location Mention Recognition dataset . it provides human- and automatically-labeled versions of tweets in order of thousands and millions of tweet . |
| Outcome: | The proposed dataset provides human- and automatically-labeled versions in order of thousands and millions of tweets. |
CULEMO: Cultural Lenses on Emotion - Benchmarking LLMs for Cross-Cultural Emotion Understanding (2025.acl-long)
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Tadesse Destaw Belay, Ahmed Haj Ahmed, Alvin C Grissom Ii, Iqra Ameer, Grigori Sidorov, Olga Kolesnikova, Seid Muhie Yimam
| Challenge: | Existing emotion benchmarks rely on keyword-based emotion recognition, overlooking cultural dimensions required for emotion understanding. |
| Approach: | They propose a benchmark to evaluate culturally-aware emotion prediction across six languages. |
| Outcome: | The proposed benchmark evaluates state-of-the-art LLMs on culture-aware emotion prediction and sentiment analysis tasks. |
Arabic Mini-ClimateGPT : A Climate Change and Sustainability Tailored Arabic LLM (2023.findings-emnlp)
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Sahal Mullappilly, Abdelrahman Shaker, Omkar Thawakar, Hisham Cholakkal, Rao Anwer, Salman Khan, Fahad Khan
| Challenge: | Recent large language models like ChatGPT and Bard excel in a wide variety of NLP tasks but are not specifically tailored for climate related domain specific information. |
| Approach: | They propose a lightweight Arabic Mini-ClimateGPT that is built on an open-source LLM and specifically fine-tuned on a conversational-style instruction tuning curated Arabic dataset Clima500-Instruct. |
| Outcome: | The proposed model surpasses the baseline LLM in 88.3% of cases during ChatGPT-based evaluation and human expert prefers it over other open-source models. |
Cultural Benchmarking of LLMs in Standard and Dialectal Arabic Dialogues (2026.acl-long)
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Muhammad Dehan Al Kautsar, Saeed Almheiri, Momina Ahsan, Bilal Elbouardi, Younes Samih, Sarfraz Ahmad, Amr Keleg, Omar El Herraoui, Kareem Elzeky, Abed Alhakim Freihat, Mohamed Anwar, Zhuohan Xie, Junhong Liang, Mohammad Rustom Al Nasar, Preslav Nakov, Fajri Koto
| Challenge: | Most benchmarks focus on short text snippets in Modern Standard Arabic (MSA), overlooking cultural nuances that naturally arise in dialogues. |
| Approach: | They propose a culturally grounded conversational dataset covering 13 Arabic-speaking countries, in both Modern Standard Arabic (MSA) and each country’s respective dialect, spanning 12 daily-life topics and 54 fine-grained subtopics. |
| Outcome: | The proposed model performs worse on all three tasks than the MSA benchmark. |
MARASTA: A Multi-dialectal Arabic Cross-domain Stance Corpus (2024.lrec-main)
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Anis Charfi, Mabrouka Ben-Sghaier, Andria Samy Raouf Atalla, Raghda Akasheh, Sara Al-Emadi, Wajdi Zaghouani
| Challenge: | Approximately half of the sentences are in Modern Standard Arabic (MSA) for each region, and the other half is in the region’s respective dialect. |
| Approach: | They propose a cross-domain and multi-dialectal stance corpus for Arabic that includes four regions in the Arab World and covers the main Arabic dialect groups. |
| Outcome: | The proposed corpus outperforms the state-of-the-art dataset in stance detection and dialect and dialect classes. |
BiMediX: Bilingual Medical Mixture of Experts LLM (2024.findings-emnlp)
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Sara Pieri, Sahal Shaji Mullappilly, Fahad Khan, Rao Anwer, Salman Khan, Timothy Baldwin, Hisham Cholakkal
| Challenge: | a new bilingual medical mixture of experts LLM is designed for seamless interaction in both English and Arabic. |
| Approach: | They propose a semi-automated English-to-Arabic translation pipeline with human refinement to ensure high-quality translations. |
| Outcome: | The proposed model outperforms state-of-the-art medical LLMs in Arabic and Arabic . it outperformed the generic Arabic-English bilingual LLM, Jais-30B by 10% and 15% . |
Preserving Fairness and Safety in Quantized LLMs Through Critical Weight Protection (2026.findings-acl)
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| Challenge: | Existing quantization methods focus on general metrics like perplexity or accuracy on standard benchmarks. |
| Approach: | They propose a method that preserves fairness- and safety-critical weights during quantization. |
| Outcome: | The proposed method reduces bias and safety degradation without costly retraining or alignment while maintaining trustworthiness while retaining efficiency. |
Jailbreaking LLMs with Arabic Transliteration and Arabizi (2024.emnlp-main)
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| Challenge: | Large Language Models (LLMs) are vulnerable to ‘jailbreak’ attacks, focusing on the Arabic language and its various forms. |
| Approach: | They propose to use Arabic transliteration and chatspeak to generate unsafe content on platforms like OpenAI GPT-4 and Anthropic Claude 3 Sonnet. |
| Outcome: | The proposed model could generate unsafe content on platforms like OpenAI GPT-4 and Anthropic Claude 3 Sonnet, highlighting the need for more comprehensive safety training across all language forms. |
JASMINE: Arabic GPT Models for Few-Shot Learning (2023.emnlp-main)
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El Moatez Billah Nagoudi, Muhammad Abdul-Mageed, AbdelRahim Elmadany, Alcides Inciarte, Md Tawkat Islam Khondaker
| Challenge: | generative pretraining (GPT) scholarship remains acutely anglocentric, leaving serious gaps in our understanding of the whole class of autoregressive models. |
| Approach: | They propose to use Arabic autoregressive models to evaluate their performance . they use a benchmark to evaluate the models and code for experimenting with them . |
| Outcome: | JASMINE is a suite of powerful Arabic autoregressive models . it shows powerful performance intrinsically and in few-shot learning on a wide range of tasks. |
LC-Eval: A Bilingual Multi-Task Evaluation Benchmark for Long-Context Understanding (2025.findings-emnlp)
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Sheikh Jubair, Arwa Omayrah, Amal Alshammari, Alhanoof Althnian, Abdulhamed Alothaimen, Norah A. Alzahrani, Shahad D. Alzaidi, Nora Al-Twairesh, Abdulmohsen Al-Thubaity
| Challenge: | Recent advances in Large Language Models (LLMs) have demonstrated sophisticated capabilities, including the ability to process and comprehend extended contexts. |
| Approach: | They propose a bilingual, multi-task evaluation benchmark designed to evaluate long-context understanding in English and Arabic. |
| Outcome: | The proposed benchmark targets context lengths ranging from 4k to over 128k tokens. |
INDOTABVQA: A Benchmark for Cross-Lingual Table Understanding in Bahasa Indonesia Documents (2026.findings-acl)
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| Challenge: | INDOTABVQA provides a benchmark for evaluating cross-lingual Table Visual Question Answering (VQA) on real-world document images in Bahasa Indonesia. |
| Approach: | They propose a benchmark for evaluating cross-lingual Table Visual Question Answering on real-world document images in Bahasa Indonesia. |
| Outcome: | The proposed model improves on a 3B model and a LoRA- finetuned 7B model on Bahasa Indonesian document images by 11.6% and 17.8% respectively. |
Splintering Nonconcatenative Languages for Better Tokenization (2025.findings-acl)
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| Challenge: | Subword tokenizers are not effective in nonconcatenative languages such as Hebrew and Arabic, where morphology is encoded in root-template patterns, or Malay and Georgian, where split affixes are common. |
| Approach: | They propose a pre-processing step which rearranges text into a linear form that better represents nonconcatenative morphologies. |
| Outcome: | The proposed method is based on measures evaluating token vocabularies in Hebrew, Arabic, and Malay, and on BERT-architecture models trained for Hebrew. |
R-BPE: Improving BPE-Tokenizers with Token Reuse (2025.emnlp-main)
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| Challenge: | Large pretrained language models prioritize high-resource languages in their vocabularies, leaving others with poor coverage. |
| Approach: | They propose a framework that reuses existing tokenizers and creates ID-based maps to resolve the new tokens of the chosen language. |
| Outcome: | The proposed framework reduces subword fertility by 24.4% on Arabic models and preserves performance on EnglishMMLU. |
QCAW 1.0: Building a Qatari Corpus of Student Argumentative Writing (2024.lrec-main)
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| Challenge: | Existing studies have highlighted the importance of and need to create learner corpora. |
| Approach: | They propose to create a Qatari corpus of argumentative writing (QCAW) the corpus contains 200,000 tokens of argumentation written by Qatari university students . |
| Outcome: | The QCAW contains 195 essays written by 195 students, 159 females and 36 males. |
NeoAraBERT: A Modern Foundation Model for Arabic Embeddings with Diacritics-Aware Tokenization and POS-Targeted Masking (2026.findings-acl)
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Chadi Abou Chakra, Hadi Khaled Hamoud, Osama Rakan Al Mraikhat, Qusai Abu Obaida, Mohamad Ballout, Fadi Zaraket
| Challenge: | NeoAraBERT is an open-source text-embedding model for Arabic. |
| Approach: | They propose to train Arabic text-embedding models on open-source datasets . they benchmarked NeoAraBERT against five top-performing Arabic models on 23 tasks . |
| Outcome: | The proposed model outperforms five other models on 23 tasks in Arabic . it shows substantial improvement on classical and modern standard Arabic compared to other models . |
Zero-Shot Context-Aware ASR for Diverse Arabic Varieties (2026.findings-acl)
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| Challenge: | Large-scale multilingual ASR has substantially improved recognition for high-resource languages. |
| Approach: | They propose a proxy-guided -best selection paradigm that conditions inference on external side information without parameter updates. |
| Outcome: | The proposed model reduces WER by 15.6% relative and recovers a fraction of oracle n-best gains on the common voice MSA testbed. |
BOUQuET : dataset, Benchmark and Open initiative for Universal Quality Evaluation in Translation (2025.emnlp-main)
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Pierre Andrews, Mikel Artetxe, Mariano Coria Meglioli, Marta R. Costa-jussà, Joe Chuang, David Dale, Mark Duppenthaler, Nathanial Paul Ekberg, Cynthia Gao, Daniel Edward Licht, Jean Maillard, Alexandre Mourachko, Christophe Ropers, Safiyyah Saleem, Eduardo Sánchez, Ioannis Tsiamas, Arina Turkatenko, Albert Ventayol-Boada, Shireen Yates
| Challenge: | BOUQUET is a multi-way, multicentric and multi-register/domain dataset and benchmark . the dataset is handcrafted in 8 non-English languages . |
| Approach: | They propose to use BOUQuET to collect a multi-way, multicentric and multi-register/domain dataset and benchmark in 8 non-English languages. |
| Outcome: | The proposed dataset is available at https://huggingface.co/datasets/facebook/bouquet. |
Multilingual Language Model Pretraining using Machine-translated Data (2025.emnlp-main)
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Jiayi Wang, Yao Lu, Maurice Weber, Max Ryabinin, David Ifeoluwa Adelani, Yihong Chen, Raphael Tang, Pontus Stenetorp
| Challenge: | Existing methods for collecting and filtering multilingual web data lead to most languages lagging behind English performance due to the Internet's English-centric nature. |
| Approach: | They propose to translate a high-quality English web corpus into nine languages and pretrain a 1.3B-parameter model on it. |
| Outcome: | The proposed model matches or outperforms multilingual LLMs of similar size across Non-English understanding and reasoning tasks despite being trained on an order of magnitude less data. |
Dialectal Coverage And Generalization in Arabic Speech Recognition (2025.acl-long)
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| Challenge: | Existing ASR systems cover the modern standard Arabic variety but fail to cover the multitude of spoken variants. |
| Approach: | They propose a suite of automatic speech recognition models optimized to recognize multiple variants of spoken Arabic. |
| Outcome: | The proposed models show coverage and performance gains compared to prior models. |
Alexandria: A Multi-Domain Dialectal Arabic Machine Translation Dataset for Culturally Inclusive and Linguistically Diverse LLMs (2026.acl-long)
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Abdellah EL Mekki, Samar M. Magdy, Houdaifa Atou, Ruwa AbuHweidi, Baraah Qawasmeh, Omer Nacar, Thikra Al-hibiri, Razan Saadie, Hamzah A. Alsayadi, Nadia Ghezaiel Hammouda, Alshima Mohammed Alkhazimi, Aya Hamod, Al-Yas Yaqoob Al-Ghafri, Wesam El-Sayed, Asila Ismail al Sharji, Mohamad Ballout, Anas Belfathi, Karim Ghaddar, Serry Sibaee, Alaa Aoun, Aeej Mohammed Aseri, Lina Abureesh, Ahlam Bashiti, Majdal Yousef, Abdulaziz Hafiz, Yehdih Mohamed, Emira Hamedtou, Brakehe Emehah, Rahaf Alhamouri, Youssef Nafea, Aya El Aatar, Walid Al-Dhabyani, Emhemed S. Hamed, Sara Shatnawi, Fakhraddin Alwajih, Khalid Elkhidir, Ashwag Alasmari, Abdurrahman Gerrio, Omar Said Alshahri, AbdelRahim A. Elmadany, Ismail Berrada, Amir Azad Adli Al-kathiri, Fadi Zaraket, Mustafa Jarrar, Yahya Mohamed EL Hadj, Hassan Alhuzali, Muhammad Abdul-Mageed
| Challenge: | Arabic is a highly diglossic language where most daily communication occurs in regional dialects rather than modern standard Arabic (MSA). |
| Approach: | They propose a large-scale, community-driven, human-translated dataset to bridge this gap . Alexandria covers 13 Arab countries and 11 high-impact domains . it provides unprecedented granularity by associating contributions with city-of-origin metadata . |
| Outcome: | The Alexandria dataset covers 13 Arab countries and 11 high-impact domains . it provides unprecedented granularity by associating contributions with city-of-origin metadata . Alexandria is a training resource and a rigorous benchmark for evaluating MT and LLMs based on the Alexandria dataset . |
Lemmatization as a Classification Task: Results from Arabic across Multiple Genres (2025.emnlp-main)
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| Challenge: | Existing tools for lemmatization in morphologically rich languages with ambiguous orthography face inconsistent standards and limited genre coverage. |
| Approach: | They propose two new approaches that frame lemmatization as classification into a Lemma-POS-Gloss tagset, leveraging machine translation and semantic clustering. |
| Outcome: | The proposed models perform better than existing models and are more interpretable, the authors show. |
AdabNER: Arabic Digital Archive Books with Nested Entity Recognition (2026.acl-long)
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| Challenge: | Named Entity Recognition (NER) is a subtask of information extraction that classifies entities into predefined categories like person names. |
| Approach: | They propose a large-scale nested Arabic Named Entity Recognition dataset . they fine-tuned five pre-trained Arabic BERT encoders in two settings . |
| Outcome: | The first large-scale nested NER dataset for Arabic literary texts is published online . the dataset yields 78,530 entity mentions, 18.96% of which are nestated . |
ZAEBUC-Spoken: A Multilingual Multidialectal Arabic-English Speech Corpus (2024.lrec-main)
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| Challenge: | a corpus of multilingual Arabic-English speech is presented in a new paper . a major bottleneck is the lack of data needed for training NLP models . |
| Approach: | They propose a multilingual multidialectal Arabic-English speech corpus with a set of guidelines for automatic speech recognition. |
| Outcome: | The proposed corpus includes two languages with Arabic and English spoken in multiple variants and Arabic and Arabic with various accents. |
Palm: A Culturally Inclusive and Linguistically Diverse Dataset for Arabic LLMs (2025.acl-long)
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Fakhraddin Alwajih, Abdellah El Mekki, Samar Mohamed Magdy, AbdelRahim A. Elmadany, Omer Nacar, El Moatez Billah Nagoudi, Reem Abdel-Salam, Hanin Atwany, Youssef Nafea, Abdulfattah Mohammed Yahya, Rahaf Alhamouri, Hamzah A. Alsayadi, Hiba Zayed, Sara Shatnawi, Serry Sibaee, Yasir Ech-chammakhy, Walid Al-Dhabyani, Marwa Mohamed Ali, Imen Jarraya, Ahmed Oumar El-Shangiti, Aisha Alraeesi, Mohammed Anwar AL-Ghrawi, Abdulrahman S. Al-Batati, Elgizouli Mohamed, Noha Taha Elgindi, Muhammed Saeed, Houdaifa Atou, Issam Ait Yahia, Abdelhak Bouayad, Mohammed Machrouh, Amal Makouar, Dania Alkawi, Mukhtar Mohamed, Safaa Taher Abdelfadil, Amine Ziad Ounnoughene, Anfel Rouabhia, Rwaa Assi, Ahmed Sorkatti, Mohamedou Cheikh Tourad, Anis Koubaa, Ismail Berrada, Mustafa Jarrar, Shady Shehata, Muhammad Abdul-Mageed
| Challenge: | a year-long community-driven project covering all 22 Arab countries evaluates the cultural and dialectal capabilities of large language models. |
| Approach: | They propose a project to evaluate the cultural and dialectal capabilities of large language models. |
| Outcome: | The project evaluates the cultural and dialectal capabilities of several frontier LLMs. |
Linear Semantic Segmentation for Low-Resource Spoken Dialects (2026.findings-acl)
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| Challenge: | Existing models for semantic segmentation are primarily developed and evaluated on high-resource written text, limiting their effectiveness on low-resourced conversational varieties. |
| Approach: | They propose a multi-genre benchmark for semantic segmentation in Arabic, focusing on dialectal discourse. |
| Outcome: | The proposed model outperforms baselines on dialectal non-news genres while performing well on high-resource written text. |
Instruction-Guided Poetry Generation in Arabic and Its Dialects (2026.findings-acl)
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Abdelrahman Sadallah, Kareem Elozeiri, Mervat Abassy, Rania Elbadry, Mohamed Anwar, Abed Alhakim Freihat, Preslav Nakov, Fajri Koto
| Challenge: | Existing literature on Arabic poetry has focused on analysis tasks such as interpretation or metadata prediction, e.g., rhyme schemes and titles. |
| Approach: | They propose to use a large-scale instruction-based dataset to generate Arabic poetry based on predefined criteria such as style and rhyme . |
| Outcome: | The proposed model can generate poetry that is aligned with user requirements, based on automated metrics and human evaluation with native Arabic speakers. |
Worldwide LiveVQA: Real-Time Visual Knowledge Seeking and Updating Across Languages (2026.findings-acl)
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| Challenge: | Existing resources for visual knowledge remain confined to English, creating a "Worldwide Knowledge Gap" eric liu: production and dissemination of knowledge exhibit a distinct trend toward decentralization and linguistic fragmentation. |
| Approach: | They propose a dataset for multilingual visual knowledge seeking and updating across ten major languages. |
| Outcome: | The proposed dataset is the first dynamic-updating dataset for multilingual visual knowledge seeking and updating across ten major languages. |
LQM: Linguistically Motivated Multidimensional Quality Metrics for Machine Translation (2026.findings-acl)
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| Challenge: | Existing MT evaluation frameworks fail to capture dialect- and culture-specific errors in diglossic languages. |
| Approach: | They propose a hierarchical error taxonomy for diagnosing MT errors through six linguistic levels: sociolinguistics, pragmatics, semantics, morphosyntax, orthography, and graphetics. |
| Outcome: | The proposed framework produces 6,113 labeled error spans across 3,495 unique erroneous sentences . it is language-agnostic and can be easily applied to or adapted for other languages. |
MorphBPE: Morphology-Aware Tokenization for Efficient LLM Training (2026.findings-acl)
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| Challenge: | Tokenization is a key design choice in modern NLP systems and a critical bottleneck for multilingual Large Language Models. |
| Approach: | They propose a tokenization extension that constrains merge operations to respect morpheme boundaries while preserving inference. |
| Outcome: | The proposed tokenization improves morphological coherence and language model cross-entropy in four languages. |