CLIP Colloquium: Antonios Anastasopoulos (GMU)

Hierarchical Approaches for Expanding NLP Language Coverage

Abstract: The availability of large multilingual pre-trained language models has opened up exciting pathways for developing NLP technologies for languages with scarce resources. In this talk I will summarize some of my group's recent work on the challenges we are still facing in the real world, such as handling unseen-during-pretraining languages, language varieties, and languages from bilingual communities, and I will show the advantage of hierarchical approaches for tackling such issues.

Bio: Antonios Anastasopoulos is an Assistant Professor in Computer Science at George Mason University. He received his PhD in Computer Science from the University of Notre Dame with a dissertation on "NLP for Endangered Languages Documentation" and then did a postdoc at Languages Technologies Institute at Carnegie Mellon University. His research is on natural language processing with a focus on low-resource settings, endangered languages, and cross-lingual learning, and is currently funded by the National Science Foundation, the National Endowment for the Humanities, the DoD, Google, Amazon, and Meta.