  Maximum-likelihood estimation (MLE) is widely used in sequence to sequence
tasks for model training. It uniformly treats the generation/prediction of each
target token as multi-class classification, and yields non-smooth prediction
probabilities: in a target sequence, some tokens are predicted with small
probabilities while other tokens are with large probabilities. According to our
empirical study, we find that the non-smoothness of the probabilities results
in low quality of generated sequences. In this paper, we propose a
sentence-wise regularization method which aims to output smooth prediction
probabilities for all the tokens in the target sequence. Our proposed method
can automatically adjust the weights and gradients of each token in one
sentence to ensure the predictions in a sequence uniformly well. Experiments on
three neural machine translation tasks and one text summarization task show
that our method outperforms conventional MLE loss on all these tasks and
achieves promising BLEU scores on WMT14 English-German and WMT17
Chinese-English translation task.
