Multilingual NLP: Cross-lingual Transfer Learning and Applications
Junjie Hu, UW-Madison, Biostatistics & Computer Science
Over the last decade, the phenomenal success of NLP systems has been mostly driven by deep neural networks and supervised machine learning approaches on a large amount of labeled data. However, it’s infeasible to annotate data under all possible real-world scenarios. As a result, these systems may fail dramatically in practice when dealing with complex textual data written in different languages, or even associated with different data modalities. In this talk, I will present work on two distinct aspects that are important to extend the generalization ability of NLP systems. First, I will present my work on XTREME that provides a platform for cross-lingual learning on 9 NLP tasks over 40 languages, and its recent extension XTREME-R. I will then demonstrate cross-lingual applications on text-video retrieval and dialog. This talk will be concluded with an overview of my research in the interdisciplinary field of AI and data science.
Bio:
Junjie Hu is an Assistant Professor of Biostatistics and Computer Science (affiliated) at the University of Wisconsin-Madison. He obtained his Ph.D. from the School of Computer Science at Carnegie Mellon University, working with Prof. Jaime Carbonell and Prof. Graham Neubig. His research lies at the intersection of natural language processing and machine learning. In particular, he works on multilingual NLP, transfer learning, and their applications in language communication and healthcare. His research has attracted media attention in outlets such as Slator, Google/Facebook AI blogs, Wired. He is the recipient of a Best Demo Paper Nomination at NAACL 2019.