arXiv ·
A study investigated language shift from Tibetan to Arabic among Tibetan families who migrated to Saudi Arabia 70 years ago. Data from 96 participants across three age groups revealed significant intergenerational differences in language use. Younger members rarely used Tibetan, while older members used it slightly more, with a p-value of .001 indicating statistical significance.
arXiv ·
This survey paper reviews the landscape of Natural Language Processing (NLP) research and applications in the Arab world. It discusses the unique challenges posed by the Arabic language, such as its morphological complexity and dialectal diversity. The paper also presents a historical overview of Arabic NLP and surveys various research areas, including machine translation, sentiment analysis, and speech recognition. Why it matters: The survey provides a comprehensive resource for researchers and practitioners interested in the current state and future directions of Arabic NLP, a field critical for enabling AI technologies to serve Arabic-speaking communities.
MBZUAI ·
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.
MBZUAI ·
This article discusses domain shift in machine learning, where testing data differs from training data, and methods to mitigate it via domain adaptation and generalization. Domain adaptation uses labeled source data and unlabeled target data. Domain generalization uses labeled data from single or multiple source domains to generalize to unseen target domains. Why it matters: Research in mitigating domain shift enhances the robustness and applicability of AI models in diverse real-world scenarios.
arXiv ·
This paper surveys the landscape of code-switched Arabic natural language processing, covering the mixture of Modern Standard Arabic, dialects, and foreign languages. It examines current efforts, challenges, and research gaps in the field. The survey also provides recommendations for future research directions in code-switched Arabic NLP. Why it matters: Understanding code-switching is crucial for developing effective language technologies that can handle the diverse linguistic landscape of the Arab world.
MBZUAI ·
Thamar Solorio from the University of Houston will discuss machine learning approaches for spontaneous human language processing. The talk will cover adapting multilingual transformers to code-switching data and using data augmentation for domain adaptation in sequence labeling tasks. Solorio will also provide an overview of other research projects at the RiTUAL lab, focusing on the scarcity of labeled data. Why it matters: This presentation addresses key challenges in Arabic NLP related to data scarcity, which is a persistent obstacle in developing effective AI applications for the region.
arXiv ·
The authors introduce Nile-Chat, a collection of LLMs (4B, 3x4B-A6B, and 12B) specifically for the Egyptian dialect, capable of understanding and generating text in both Arabic and Latin scripts. A novel language adaptation approach using the Branch-Train-MiX strategy is used to merge script-specialized experts into a single MoE model. Nile-Chat models outperform multilingual and Arabic LLMs like LLaMa, Jais, and ALLaM on newly introduced Egyptian benchmarks, with the 12B model achieving a 14.4% performance gain over Qwen2.5-14B-Instruct on Latin-script benchmarks; all resources are publicly available. Why it matters: This work addresses the overlooked aspect of adapting LLMs to dual-script languages, providing a methodology for creating more inclusive and representative language models in the Arabic-speaking world.