Tom M. Mitchell from Carnegie Mellon University discussed using machine learning to study how the brain processes natural language, using fMRI and MEG to record brain activity while reading text. The research explores neural encodings of word meaning, information flow during word comprehension, and how meanings of words combine in sentences and stories. He also touched on how understanding of the brain aligns with current AI approaches to NLP. Why it matters: This interdisciplinary research could bridge the gap between neuroscience and AI, potentially leading to more human-like NLP models.
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 Professor Preslav Nakov is researching methods to combat fake news and online disinformation through NLP techniques. His work focuses on detecting harmful memes and identifying the stance of individuals regarding disinformation. Four of Nakov’s recent papers on these topics were presented at NAACL 2022. Why it matters: This research aims to mitigate the impact of weaponized news and online manipulation, contributing to a more trustworthy information environment in the region and globally.
NYUAD and MBZUAI co-hosted the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP) in Abu Dhabi from December 7-11. EMNLP is a top-tier NLP and AI conference organized by the ACL special interest group on linguistic data (SIGDAT). MBZUAI's Natural Language Processing Department is actively developing NLP datasets and methods to solve social problems. Why it matters: Hosting EMNLP in the UAE highlights the growing importance of NLP research in the region and the increasing contributions of local institutions like MBZUAI to the field.
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.
Mausam, head of Yardi School of AI at IIT Delhi and affiliate professor at University of Washington, will discuss Neuro-Symbolic AI. The talk will cover recent research threads with applications in NLP, probabilistic decision-making, and constraint satisfaction. Mausam's research explores neuro-symbolic machine learning, computer vision for radiology, NLP for robotics, multilingual NLP, and intelligent information systems. Why it matters: Neuro-Symbolic AI is gaining importance as it combines the strengths of neural and symbolic approaches, potentially leading to more robust and explainable AI systems.
MBZUAI has appointed Professor Timothy Baldwin as Associate Provost and acting chair of its new NLP Department. Baldwin will focus on strengthening the curriculum and building a world-class faculty team. He previously spent 17 years at the University of Melbourne. Why it matters: The recruitment signals MBZUAI's commitment to becoming a leading center for NLP research and education in the region.
A new paper coauthored by researchers at The University of Melbourne and MBZUAI explores disagreement in human annotation for AI training. The paper treats disagreement as a signal (human label variation or HLV) rather than noise, and proposes new evaluation metrics based on fuzzy set theory. These metrics adapt accuracy and F-score to cases where multiple labels may plausibly apply, aligning model output with the distribution of human judgments. Why it matters: This research addresses a key challenge in NLP by accounting for the inherent ambiguity in human language, potentially leading to more robust and human-aligned AI systems.