Language Models not just for Pre-training: Fast Online Neural Noisy Channel Modeling
This work addresses the efficiency problem for machine translation practitioners by making noisy channel modeling faster and more accurate, though it is incremental as it builds on existing noisy channel ideas.
The paper tackled the slow inference speed of noisy channel modeling in neural machine translation by introducing efficient approximations, achieving a new state of the art on WMT Romanian-English translation with improved accuracy and speed comparable to strong ensembles.
Pre-training models on vast quantities of unlabeled data has emerged as an effective approach to improving accuracy on many NLP tasks. On the other hand, traditional machine translation has a long history of leveraging unlabeled data through noisy channel modeling. The same idea has recently been shown to achieve strong improvements for neural machine translation. Unfortunately, naïve noisy channel modeling with modern sequence to sequence models is up to an order of magnitude slower than alternatives. We address this issue by introducing efficient approximations to make inference with the noisy channel approach as fast as strong ensembles while increasing accuracy. We also show that the noisy channel approach can outperform strong pre-training results by achieving a new state of the art on WMT Romanian-English translation.