We frame the task of machine translation evaluation as one of scoring machine translation
output with a sequence-to-sequence paraphraser, conditioned on a human reference. We …

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 …

Recent years have seen big advances in the field of sentence-level quality estimation (QE),
largely as a result of using neural-based architectures. However, the majority of these …

We introduce OpenKiwi, a PyTorch-based open source framework for translation quality
estimation. OpenKiwi supports training and testing of word-level and sentence-level quality …

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 …

While back-translation is simple and effective in exploiting abundant monolingual corpora to
improve low-resource neural machine translation (NMT), the synthetic bilingual corpora …

Machine Translation Quality Estimation (MTQE) is the task of estimating the quality of
machine-translated text in real time without the need for reference translations, which is of …

Most studies on word-level Quality Estimation (QE) of machine translation focus on
language-specific models. The obvious disadvantages of these approaches are the need for …

This paper presents our submission to the WMT2020 Shared Task on Quality Estimation
(QE). We participate in Task and Task 2 focusing on sentence-level prediction. We explore …

We propose the use of pre-trained embeddings as features of a regression model for
sentence-level quality estimation of machine translation. In our work we combine freely …