We present COMET, a neural framework for training multilingual machine translation
evaluation models which obtains new state-of-the-art levels of correlation with human …

This paper presents the results of the WMT17 shared tasks, which included three machine
translation (MT) tasks (news, biomedical, and multimodal), two evaluation tasks (metrics and …

This article presents a review of the evolution of automatic post-editing, a term that describes
methods to improve the output of machine translation systems, based on knowledge …

We present the joint contribution of IST and Unbabel to the WMT 2022 Shared Task on
Quality Estimation (QE). Our team participated on all three subtasks:(i) Sentence and Word …

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 …

Modern sentence-level NMT systems often produce plausible translations of isolated
sentences. However, when put in context, these translations may end up being inconsistent …

We present the results from the 5th round of the WMT task on MT Automatic Post-Editing.
The task consists in automatically correcting the output of a “black-box” machine translation …

Multimodal machine translation involves drawing information from more than one modality,
based on the assumption that the additional modalities will contain useful alternative views …

We present the contribution of the Unbabel team to the WMT 2020 Shared Task on Metrics.
We intend to participate on the segment-level, document-level and system-level tracks on all …

Abstract Predicting Machine Translation (MT) quality can help in many practical tasks such
as MT post-editing. The performance of Quality Estimation (QE) methods has drastically …