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Exploring Prediction Uncertainty in Machine Translation Quality Estimation
[article]
2016
arXiv
pre-print
Machine Translation Quality Estimation is a notoriously difficult task, which lessens its usefulness in real-world translation environments. Such scenarios can be improved if quality predictions are accompanied by a measure of uncertainty. However, models in this task are traditionally evaluated only in terms of point estimate metrics, which do not take prediction uncertainty into account. We investigate probabilistic methods for Quality Estimation that can provide well-calibrated uncertainty
arXiv:1606.09600v1
fatcat:kqallfdyzrbyhcle2flgvaj3he