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.
Dr. Teresa Lynn from Dublin City University (DCU) discussed the challenges in developing NLP tools for Irish, a low-resource language facing digital extinction. She highlighted the lack of speech and language applications and fundamental language resources for Irish. Lynn also mentioned her work at DCU on the GaelTech project and her involvement in the European Language Equality project. Why it matters: The development of NLP tools for low-resource languages like Irish is crucial for preserving linguistic diversity and preventing digital marginalization in the AI era.
MBZUAI's Dr. Fajri Koto presented research on overcoming challenges in NLP for underrepresented languages. His work includes creating multilingual datasets for Indonesian languages by engaging native speakers and finding that direct composition yields better results than translation. He also discussed vocabulary adaptation and zero-shot learning to address computational resource limitations, and emphasized the importance of datasets with local context for evaluating LLMs. Why it matters: This research addresses critical gaps in NLP for low-resource languages, providing insights and techniques to improve performance and cultural relevance in multilingual AI models within the region and globally.
This paper introduces two shared tasks for abusive and threatening language detection in Urdu, a low-resource language with over 170 million speakers. The tasks involve binary classification of Urdu tweets into Abusive/Non-Abusive and Threatening/Non-Threatening categories, respectively. Datasets of 2400/6000 training tweets and 1100/3950 testing tweets were created and manually annotated, along with logistic regression and BERT-based baselines. 21 teams participated and the best systems achieved F1-scores of 0.880 and 0.545 on the abusive and threatening language tasks, respectively, with m-BERT showing the best performance.
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.