2 citations · 3 across the 4 of their papers we have counts for
8 papers · 1 filter
An Interdisciplinary Approach to Human-Centered Machine Translation
Marine Carpuat, Omri Asscher, Kalika Bali +17
Machine Translation (MT) tools are widely used today, often in contexts where professional translators are not present. Despite progress in MT technology, a gap persists between sy…
Are We Paying Attention to Her? Investigating Gender Disambiguation and Attention in Machine Translation
Chiara Manna, Afra Alishahi, Frédéric Blain +1
While gender bias in modern Neural Machine Translation (NMT) systems has received much attention, traditional evaluation metrics do not to fully capture the extent to which these s…
Tailoring Domain Adaptation for Machine Translation Quality Estimation
Javad Pourmostafa Roshan Sharami, Dimitar Shterionov, Frédéric Blain +4
While quality estimation (QE) can play an important role in the translation process, its effectiveness relies on the availability and quality of training data. For QE in particular…
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…