743 research outputs found
Improving End-to-End Speech Recognition with Policy Learning
Connectionist temporal classification (CTC) is widely used for maximum
likelihood learning in end-to-end speech recognition models. However, there is
usually a disparity between the negative maximum likelihood and the performance
metric used in speech recognition, e.g., word error rate (WER). This results in
a mismatch between the objective function and metric during training. We show
that the above problem can be mitigated by jointly training with maximum
likelihood and policy gradient. In particular, with policy learning we are able
to directly optimize on the (otherwise non-differentiable) performance metric.
We show that joint training improves relative performance by 4% to 13% for our
end-to-end model as compared to the same model learned through maximum
likelihood. The model achieves 5.53% WER on Wall Street Journal dataset, and
5.42% and 14.70% on Librispeech test-clean and test-other set, respectively
Learning Hard Alignments with Variational Inference
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
Learning to Translate in Real-time with Neural Machine Translation
Translating in real-time, a.k.a. simultaneous translation, outputs
translation words before the input sentence ends, which is a challenging
problem for conventional machine translation methods. We propose a neural
machine translation (NMT) framework for simultaneous translation in which an
agent learns to make decisions on when to translate from the interaction with a
pre-trained NMT environment. To trade off quality and delay, we extensively
explore various targets for delay and design a method for beam-search
applicable in the simultaneous MT setting. Experiments against state-of-the-art
baselines on two language pairs demonstrate the efficacy of the proposed
framework both quantitatively and qualitatively.Comment: 10 pages, camera read
- …