  Neural machine translation (NMT) models are typically trained using a softmax
cross-entropy loss where the softmax distribution is compared against smoothed
gold labels. In low-resource scenarios, NMT models tend to over-fit because the
softmax distribution quickly approaches the gold label distribution. To address
this issue, we propose to divide the logits by a temperature coefficient, prior
to applying softmax, during training. In our experiments on 11 language pairs
in the Asian Language Treebank dataset and the WMT 2019 English-to-German
translation task, we observed significant improvements in translation quality
by up to 3.9 BLEU points. Furthermore, softmax tempering makes the greedy
search to be as good as beam search decoding in terms of translation quality,
enabling 1.5 to 3.5 times speed-up. We also study the impact of softmax
tempering on multilingual NMT and recurrently stacked NMT, both of which aim to
reduce the NMT model size by parameter sharing thereby verifying the utility of
temperature in developing compact NMT models. Finally, an analysis of softmax
entropies and gradients reveal the impact of our method on the internal
behavior of NMT models.
