Imitation Learning and its Application to Natural Language Generation

Dec 9, 2019



Imitation learning is a learning paradigm that interpolates reinforcement learning on one extreme and supervised learning on the other extreme. In the specific case of generating structured outputs--as in natural language generation--imitation learning allows us to train generation policies with neither strong supervision on the detailed generation procedure (as would be required in supervised learning) nor with only a sparse reward signal (as in reinforcement learning). Imitation learning accomplishes this by exploiting the availability of potentially suboptimal "experts" that provide supervision along an execution trajectory of the policy. In the first part of this tutorial, we overview the paradigm of imitation learning and a suite of practical imitation learning algorithms. We then consider the specific application of natural language generation, framing this problem as a sequential decision making process. Under this view, we demonstrate how imitation learning could be successfully applied to natural language generation and open the door to a range of possible ways to learn policies that generate natural language sentences beyond naive left-to-right autoregressive generation.



About NIPS 2019

Neural Information Processing Systems (NeurIPS) is a multi-track machine learning and computational neuroscience conference that includes invited talks, demonstrations, symposia and oral and poster presentations of refereed papers. Following the conference, there are workshops which provide a less formal setting.

Store presentation

Should this presentation be stored for 1000 years?

How do we store presentations

Total of 0 viewers voted for saving the presentation to eternal vault which is 0.0%


Recommended Videos

Presentations on similar topic, category or speaker

Interested in talks like this? Follow NIPS 2019