Title: Learning Hard Alignments with Variational Inference,
Abstract: 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.