Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of
uncertainties during both optimization and decision making processes. They have been …

Neural Machine Translation (NMT) has seen tremendous growth in the last ten years since
the early 2000s and has already entered a mature phase. While considered the most widely …

There has been significant research done on developing methods for improving robustness
to distributional shift and uncertainty estimation. In contrast, only limited work has examined …

Quality Estimation (QE) is an important component in making Machine Translation (MT)
useful in real-world applications, as it is aimed to inform the user on the quality of the MT …

Abstract Machine translation (MT) is an important sub-field of natural language processing
that aims to translate natural languages using computers. In recent years, end-to-end neural …

Uncertainty estimation is important for ensuring safety and robustness of AI systems. While
most research in the area has focused on un-structured prediction tasks, limited work has …

Quantifying the uncertainty of supervised learning models plays an important role in making
more reliable predictions. Epistemic uncertainty, which usually is due to insufficient …

Neural machine translation (NMT) has proven to be facilitated by curriculum learning which
presents examples in an easy-to-hard order at different training stages. The keys lie in the …

Confidence calibration, which aims to make model predictions equal to the true correctness
measures, is important for neural machine translation (NMT) because it is able to offer useful …

As a main field of artificial intelligence, natural language processing (NLP) has achieved
remarkable success via deep neural networks. Plenty of NLP tasks have been addressed in …