Modern learning architectures for natural language processing have been very successful in incorporating a huge amount of texts into their parameters. However, by and large, such models store and use knowledge in distributed and decentralized ways. This proves unreliable and makes the models ill-suited for knowledge-intensive tasks that require reasoning over factual information in linguistic expressions. In this talk, I will give a few examples of exploring alternative architectures to tackle those challenges. In particular, we can improve the performance of such (language) models by representing, storing and accessing knowledge in a dedicated memory component.
This talk is based on several joint works with Yury Zemlyanskiy (Google Research), Michiel de Jong (USC and Google Research), William Cohen (Google Research and CMU) and our other collaborators in Google Research.
Fei is a research scientist at Google Research. Before that, he was a Professor of Computer Science at University of Southern California. His primary research interests are machine learning and its application to various AI problems: speech and language processing, computer vision, robotics and recently weather forecast and climate modeling. He has a PhD (2007) from Computer and Information Science from U. of Pennsylvania and B.Sc and M.Sc in Biomedical Engineering from Southeast University (Nanjing, China).