5 papers
Pushing the Right Buttons: Adversarial Evaluation of Quality Estimation
Diptesh Kanojia, Marina Fomicheva, Tharindu Ranasinghe +3
Current Machine Translation (MT) systems achieve very good results on a growing variety of language pairs and datasets. However, they are known to produce fluent translation output…
Knowledge Distillation for Quality Estimation
Amit Gajbhiye, Marina Fomicheva, Fernando Alva-Manchego +4
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, su…
Backtranslation Feedback Improves User Confidence in MT, Not Quality
Vilém Zouhar, Michal Novák, Matúš Žilinec +7
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, bey…
MLQE-PE: A Multilingual Quality Estimation and Post-Editing Dataset
Marina Fomicheva, Shuo Sun, Erick Fonseca +7
We present MLQE-PE, a new dataset for Machine Translation (MT) Quality Estimation (QE) and Automatic Post-Editing (APE). The dataset contains eleven language pairs, with human labe…
Unsupervised Quality Estimation for Neural Machine Translation
Marina Fomicheva, Shuo Sun, Lisa Yankovskaya +6
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 o…