Tutorial
Imitation Learning and its Application to Natural Language Generation
Kyunghyun Cho · Hal Daumé III

Mon Dec 9th 08:30 -- 10:30 AM @ West Hall C + B3

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.

Author Information

Kyunghyun Cho (New York University)

Kyunghyun Cho is an associate professor of computer science and data science at New York University and a research scientist at Facebook AI Research. He was a postdoctoral fellow at the Université de Montréal until summer 2015 under the supervision of Prof. Yoshua Bengio, and received PhD and MSc degrees from Aalto University early 2014 under the supervision of Prof. Juha Karhunen, Dr. Tapani Raiko and Dr. Alexander Ilin. He tries his best to find a balance among machine learning, natural language processing, and life, but almost always fails to do so.

Hal Daumé III (Microsoft Research & University of Maryland)

Hal Daumé III wields a professor appointment in Computer Science and Language Science at the University of Maryland, and spends time as a principal researcher in the machine learning group and fairness group at Microsoft Research in New York City. He and his wonderful advisees study questions related to how to get machines to become more adept at human language, by developing models and algorithms that allow them to learn from data. The two major questions that really drive their research these days are: (1) how can we get computers to learn language through natural interaction with people/users? and (2) how can we do this in a way that promotes fairness, transparency and explainability in the learned models?

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