[HTML][HTML] A review of uncertainty quantification in deep learning: Techniques, applications and challenges
Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of
uncertainties during both optimization and decision making processes. They have been …
uncertainties during both optimization and decision making processes. They have been …
Neural machine translation for low-resource languages: A survey
S Ranathunga, ESA Lee, M Prifti Skenduli… - ACM Computing …, 2023 - dl.acm.org
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 …
the early 2000s and has already entered a mature phase. While considered the most widely …
Unsupervised quality estimation for neural machine translation
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 …
useful in real-world applications, as it is aimed to inform the user on the quality of the MT …
[HTML][HTML] Neural machine translation: A review of methods, resources, and tools
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 …
that aims to translate natural languages using computers. In recent years, end-to-end neural …
Uncertainty estimation in autoregressive structured prediction
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 …
most research in the area has focused on un-structured prediction tasks, limited work has …
A survey on epistemic (model) uncertainty in supervised learning: Recent advances and applications
Quantifying the uncertainty of supervised learning models plays an important role in making
more reliable predictions. Epistemic uncertainty, which usually is due to insufficient …
more reliable predictions. Epistemic uncertainty, which usually is due to insufficient …
Uncertainty-aware curriculum learning for neural machine translation
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 …
presents examples in an easy-to-hard order at different training stages. The keys lie in the …
On the inference calibration of neural machine translation
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 …
measures, is important for neural machine translation (NMT) because it is able to offer useful …
Uncertainty in natural language processing: Sources, quantification, and applications
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 …
remarkable success via deep neural networks. Plenty of NLP tasks have been addressed in …