5 citations · 5 across the 5 of their papers we have counts for
6 papers · 1 filter
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…
Classification-based Quality Estimation: Small and Efficient Models for Real-world Applications
Shuo Sun, Ahmed El-Kishky, Vishrav Chaudhary +3
Sentence-level Quality estimation (QE) of machine translation is traditionally formulated as a regression task, and the performance of QE models is typically measured by Pearson co…
Translation Error Detection as Rationale Extraction
Marina Fomicheva, Lucia Specia, Nikolaos Aletras
Recent Quality Estimation (QE) models based on multilingual pre-trained representations have achieved very competitive results when predicting the overall quality of translated sen…
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…
What Makes a Scientific Paper be Accepted for Publication?
Panagiotis Fytas, Georgios Rizos, Lucia Specia
Despite peer-reviewing being an essential component of academia since the 1600s, it has repeatedly received criticisms for lack of transparency and consistency. We posit that recen…
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…