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Found 6 papers, 3 papers with code
BERGAMOT-LATTE Submissions for the WMT20 Quality Estimation Shared Task
We explore (a) a black-box approach to QE based on pre-trained representations; and (b) glass-box approaches that leverage various indicators that can be extracted from the neural MT systems.
Direct Exploitation of Attention Weights for Translation Quality Estimation
The paper presents our submission to the WMT2021 Shared Task on Quality Estimation (QE).
Backtranslation Feedback Improves User Confidence in MT, Not Quality
Translating text into a language unknown to the text's author, dubbed outbound translation, is a modern need for which the user experience has significant room for improvement, beyond the basic machine translation facility.
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 output at test time.
A Study in Improving BLEU Reference Coverage with Diverse Automatic Paraphrasing
We investigate a long-perceived shortcoming in the typical use of BLEU: its reliance on a single reference.
Findings of the WMT 2019 Shared Tasks on Quality Estimation
We report the results of the WMT19 shared task on Quality Estimation, i. e. the task of predicting the quality of the output of machine translation systems given just the source text and the hypothesis translations.
