  Confidence estimation aims to quantify the confidence of the model
prediction, providing an expectation of success. A well-calibrated confidence
estimate enables accurate failure prediction and proper risk measurement when
given noisy samples and out-of-distribution data in real-world settings.
However, this task remains a severe challenge for neural machine translation
(NMT), where probabilities from softmax distribution fail to describe when the
model is probably mistaken. To address this problem, we propose an unsupervised
confidence estimate learning jointly with the training of the NMT model. We
explain confidence as how many hints the NMT model needs to make a correct
prediction, and more hints indicate low confidence. Specifically, the NMT model
is given the option to ask for hints to improve translation accuracy at the
cost of some slight penalty. Then, we approximate their level of confidence by
counting the number of hints the model uses. We demonstrate that our learned
confidence estimate achieves high accuracy on extensive sentence/word-level
quality estimation tasks. Analytical results verify that our confidence
estimate can correctly assess underlying risk in two real-world scenarios: (1)
discovering noisy samples and (2) detecting out-of-domain data. We further
propose a novel confidence-based instance-specific label smoothing approach
based on our learned confidence estimate, which outperforms standard label
smoothing.
