  Scheduled sampling is an effective method to alleviate the exposure bias
problem of neural machine translation. It simulates the inference scene by
randomly replacing ground-truth target input tokens with predicted ones during
training. Despite its success, its critical schedule strategies are merely
based on training steps, ignoring the real-time model competence, which limits
its potential performance and convergence speed. To address this issue, we
propose confidence-aware scheduled sampling. Specifically, we quantify
real-time model competence by the confidence of model predictions, based on
which we design fine-grained schedule strategies. In this way, the model is
exactly exposed to predicted tokens for high-confidence positions and still
ground-truth tokens for low-confidence positions. Moreover, we observe vanilla
scheduled sampling suffers from degenerating into the original teacher forcing
mode since most predicted tokens are the same as ground-truth tokens.
Therefore, under the above confidence-aware strategy, we further expose more
noisy tokens (e.g., wordy and incorrect word order) instead of predicted ones
for high-confidence token positions. We evaluate our approach on the
Transformer and conduct experiments on large-scale WMT 2014 English-German, WMT
2014 English-French, and WMT 2019 Chinese-English. Results show that our
approach significantly outperforms the Transformer and vanilla scheduled
sampling on both translation quality and convergence speed.
