A Generalized Framework of Sequence Generation

Speaker: Kyunghyun Cho (NYU)

Date and Time: Friday, October 11, 2019 at 10:00 am

Place: 4405 Siebel Center

Abstract:

In this talk, I will describe a generalized framework under which various sequence generation approaches could be formulated. From this generalized framework, I will derive some of the recently proposed approaches to sequence generation beyond conventional left-to-right monotonic generation. They include parallel decoding via iterative refinement, latent-variable non-autoregressive sequence models, masked language model generation and non-monotonic sequential generation.

Bio:

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 University of Montreal 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.