This paper presents a methodology for digitizing and encoding the Al-Mawrid Arabic-English dictionary using the ISO Lexical Markup Framework (LMF) and TEI Lex-0 guidelines. The research resolves structural ambiguities and inconsistencies, achieving a structural parsing accuracy of 91% and high precision/recall for information extraction, such as 85% precision for synonyms. It also discusses limitations of TEI Lex-0 for Arabic phenomena and explores Linguistic Linked Open Data (LLOD) integration. Why it matters: This work provides a crucial, standardized computational lexicon for Arabic, addressing a significant gap in Arabic lexical infrastructure and offering a reproducible workflow for retro-digitization efforts in Arabic NLP and Digital Humanities.
This paper presents a methodology for digitizing and encoding the Al-Mawrid Arabic-English dictionary, transforming it into a standardized computational lexicon using the ISO Lexical Markup Framework (LMF) and TEI Lex-0 guidelines. The research, based on an empirical analysis of the letter Ayn (4.6% of the dictionary), achieved a structural parsing accuracy of 91%. Quantitative evaluation showed high performance for information extraction rules, including 85% precision and 98% recall for synonyms. Why it matters: This work addresses a significant gap in Arabic lexical infrastructure, providing an interoperable, machine-tractable resource and a reproducible workflow for retro-digitizing complex legacy bilingual lexicons for Arabic NLP and Digital Humanities.
The Hala technical report introduces a family of Arabic-centric instruction and translation models developed using a translate-and-tune pipeline. A strong Arabic-English teacher model is compressed to FP8 and used to create bilingual supervision data. The LFM2-1.2B model is fine-tuned on this data and used to translate English instruction sets into Arabic, creating a million-scale corpus. Why it matters: The release of models, data, evaluation tools, and recipes will accelerate research and development in Arabic NLP, providing valuable resources for the community.
MBZUAI releases BiMediX2, a bilingual (Arabic-English) Bio-Medical Large Multimodal Model, along with the BiMed-V dataset (1.6M samples) and BiMed-MBench evaluation benchmark. BiMediX2 supports multi-turn conversation in Arabic and English and handles diverse medical imaging modalities. The model achieves state-of-the-art results on medical LLM and LMM benchmarks, outperforming existing methods and GPT-4 in specific evaluations.
The paper introduces Juhaina, a 9.24B parameter Arabic-English bilingual LLM trained with an 8,192 token context window. It identifies limitations in the Open Arabic LLM Leaderboard (OALL) and proposes a new benchmark, CamelEval, for more comprehensive evaluation. Juhaina outperforms models like Llama and Gemma in generating helpful Arabic responses and understanding cultural nuances. Why it matters: This culturally-aligned LLM and associated benchmark could significantly advance Arabic NLP and democratize AI access for Arabic speakers.
MBZUAI researchers, in collaboration with Monash University, have introduced ArEnAV, a new dataset for deepfake detection featuring Arabic-English code-switching. The dataset comprises 765 hours of manipulated YouTube videos, incorporating intra-utterance code-switching and dialect variations. Experiments showed that code-switching significantly reduces the performance of existing deepfake detectors. Why it matters: This work addresses a critical gap in AI's ability to handle linguistic diversity, particularly in regions where code-switching is prevalent, enhancing the reliability of deepfake detection in real-world scenarios.
This paper describes QCRI's machine translation systems for the IWSLT 2016 evaluation campaign, focusing on Arabic-English and English-Arabic tracks. They built both Phrase-based and Neural machine translation models. A Neural MT system, trained by stacking data from different genres through fine-tuning, and applying ensemble over 8 models, outperformed a strong phrase-based system by 2 BLEU points in the Arabic->English direction. Why it matters: The research highlights the early promise of neural machine translation for Arabic language pairs, demonstrating its potential to surpass traditional methods.
Injy Hamed from NYU Abu Dhabi's CAMeL Lab presented work on Egyptian Arabic-English code-switching for ASR and MT. She discussed the ArzEn-ST speech translation corpus and compared end-to-end and hybrid systems for ASR. For MT, she presented data augmentation and word segmentation techniques to handle data scarcity, also addressing ASR evaluation challenges in code-switching. Why it matters: Research into code-switching is crucial for building NLP systems capable of processing real-world language use in the Arab world.