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Learning Hard Alignments with Variational Inference by Lawson, Dieterich; Chiu, Chung-Cheng; Tucker, George; Raffel, Colin; Swersky, Kevin; Jaitly, Navdeep is a scholarly article available to read on EtoBox.

What is Learning Hard Alignments with Variational Inference about?

There has recently been significant interest in hard attention models for tasks such as object recognition, visual captioning and speech recognition. Hard attention can offer benefits over soft attention such as decreased computational cost, but training hard attention models can be difficult because of the discrete latent variables they introduce. Previous work used REINFORCE and Q-learning to approach these issues, but those methods can provide high-variance gradient estimates and be slow to train. In this paper, we tackle the problem of learning hard attention for a sequential task using variational inference methods, specifically the recently introduced VIMCO and NVIL. Furthermore, we propose a novel baseline that adapts VIMCO to this setting. We demonstrate our method on a phoneme recognition task in clean and noisy environments and show that our method outperforms REINFORCE, with the difference being greater for a more complicated task.

Author

Lawson, Dieterich; Chiu, Chung-Cheng; Tucker, George; Raffel, Colin; Swersky, Kevin; Jaitly, Navdeep

Published

2017

Language

EN