The 31st International Conference on Computational Linguistics (COLING 2025) will be held in Abu Dhabi in January 2025, hosted by Mohamed bin Zayed University of Artificial Intelligence (MBZUAI). COLING is a major biennial NLP and AI conference that brings together leaders from research centers, academia, and industry. The conference will feature keynote talks, presentations, workshops, and tutorials, with 1,500 expected participants. Why it matters: Hosting COLING underscores the UAE's growing role in AI and NLP research and provides a platform to address regional linguistic challenges and advance AI technologies.
The 31st International Conference on Computational Linguistics (COLING 2025) is being held in Abu Dhabi from January 18-24, hosted by MBZUAI. The conference features paper presentations, demonstrations, keynote speeches, workshops, and tutorials, with over 1,500 attendees. MBZUAI faculty and students contributed 22 papers to the conference, including research on fact-checking and cross-cultural content. Why it matters: Hosting COLING 2025 highlights the UAE's growing role as a hub for AI and NLP research, particularly in Arabic language processing.
The first Workshop on Language Models for Low-Resource Languages (LoResLM 2025) was held in Abu Dhabi as part of COLING 2025. It provided a forum for researchers to share work on language models for low-resource languages. The workshop accepted 35 papers from 52 submissions, covering diverse languages and research areas.
The GenAI Content Detection Task 1 is a shared task on detecting machine-generated text, featuring monolingual (English) and multilingual subtasks. The task, part of the GenAI workshop at COLING 2025, attracted 36 teams for the English subtask and 26 for the multilingual one. The organizers provide a detailed overview of the data, results, system rankings, and analysis of the submitted systems.
MBZUAI's Provost, Tim Baldwin, provides six predictions for AI in 2025, highlighting the rise of agentic AI systems capable of performing actions on behalf of users. He notes the recent release of open-weight reasoning models like DeepSeek's R1 and OpenAI's o3-mini, emphasizing the dynamic nature of the field. Baldwin stresses the potential benefits of agentic AI, such as automating complex tasks like travel planning, while also cautioning about the need for careful deployment due to unforeseen outcomes. Why it matters: The predictions provide insight into the near-term trajectory of AI development and deployment, particularly regarding AI agents, and highlights the role of a UAE university in shaping the discussion around AI innovation.
Project LITMUS explores predicting cross-lingual transfer accuracy in multilingual language models, even without test data in target languages. The goal is to estimate model performance in low-resource languages and optimize training data for desired cross-lingual performance. This research aims to identify factors influencing cross-lingual transfer, contributing to linguistically fair MMLMs. Why it matters: Improving cross-lingual transfer is vital for creating more equitable and effective multilingual AI systems, especially for languages with limited resources.
The Qatar Computing Research Institute (QCRI) has released SpokenNativQA, a multilingual spoken question-answering dataset for evaluating LLMs in conversational settings. The dataset contains 33,000 naturally spoken questions and answers across multiple languages, including low-resource and dialect-rich languages. It aims to address the limitations of text-based QA datasets by incorporating speech variability, accents, and linguistic diversity. Why it matters: This benchmark enables more robust evaluation of LLMs in speech-based interactions, particularly for Arabic dialects and other low-resource languages.
The AraFinNLP 2024 shared task introduced two subtasks focused on Arabic financial NLP: multi-dialect intent detection and cross-dialect translation with intent preservation. It utilized the updated ArBanking77 dataset, containing 39k parallel queries in MSA and four dialects, labeled with 77 banking-related intents. 45 teams registered, with 11 participating in intent detection (achieving a top F1 score of 0.8773) and only 1 team attempting translation (achieving a BLEU score of 1.667). Why it matters: This initiative addresses the need for specialized Arabic NLP tools in the growing Arab financial sector, promoting advancements in areas like banking chatbots and machine translation.