Harnessing the synergy between natural language and interactive learning

For most of the statistical ML era, the areas of computational linguistics and reinforcement learning (RL) have been studied separately. With the rise of deep learning, we now have tools that can leverage large amounts of data across multiple modalities. In this talk, I make the case for building holistic AI systems that learn by simultaneously utilizing signals from both language and environmental feedback. While RL has been used in recent work to help understand language, I will demonstrate that language can also help agents learn control policies that generalize over domains. Developing agents that can efficiently harness this synergy between language understanding and policy learning will be crucial for our progress towards stronger AI systems.