Please join us in welcoming Kiante Brantley.
Natural language processing (NLP) systems that learn from feedback are essential to help real-world users with tasks. However, most NLP models are built from static data, unable to learn from feedback, but are deployed in environments where feedback signals exist. In this talk, I will focus on ideas for incorporating interactive machine learning into NLP. First, I will describe a new interactive machine-learning framework that combines large language models in NLP with modern reinforcement learning techniques. In the second part of the talk, I will describe a new framework for studying language-conditioned reinforcement learning in visual environments.
