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Found 14 papers, 7 papers with code
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.
MLQE-PE: A Multilingual Quality Estimation and Post-Editing Dataset
We present MLQE-PE, a new dataset for Machine Translation (MT) Quality Estimation (QE) and Automatic Post-Editing (APE).
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.
Pushing the Right Buttons: Adversarial Evaluation of Quality Estimation
However, this ability is yet to be tested in the current evaluation practices, where QE systems are assessed only in terms of their correlation with human judgements.
Knowledge Distillation for Quality Estimation
Quality Estimation (QE) is the task of automatically predicting Machine Translation quality in the absence of reference translations, making it applicable in real-time settings, such as translating online social media conversations.
deepQuest-py: Large and Distilled Models for Quality Estimation
We introduce deepQuest-py, a framework for training and evaluation of large and light-weight models for Quality Estimation (QE).
Tailoring Domain Adaptation for Machine Translation Quality Estimation
While quality estimation (QE) can play an important role in the translation process, its effectiveness relies on the availability and quality of training data.
Findings of the WMT 2020 Shared Task on Quality Estimation
We report the results of the WMT20 shared task on Quality Estimation, where the challenge is to predict the quality of the output of neural machine translation systems at the word, sentence and document levels.
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.
Findings of the WMT 2021 Shared Task on Quality Estimation
We report the results of the WMT 2021 shared task on Quality Estimation, where the challenge is to predict the quality of the output of neural machine translation systems at the word and sentence levels.
DORE: A Dataset For Portuguese Definition Generation
In this research, we fill this gap by introducing DORE; the first dataset for Definition MOdelling for PoRtuguEse containing more than 100, 000 definitions.
