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 paper presents the results of the WMT16 shared tasks, which included five machine
translation (MT) tasks (standard news, IT-domain, biomedical, multimodal, pronoun), three …

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 …

In this paper, we present a two-stage neural quality estimation model that uses multilevel
task learning for translation quality estimation (QE) at the sentence, word, and phrase levels …

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 …

Recently, quality estimation has been attracting increasing interest from machine translation
researchers, aiming at finding a good estimator for the “quality” of machine translation …

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 …

Translation quality estimation is a task of growing importance in NLP, due to its potential to
reduce post-editing human effort in disruptive ways. However, this potential is currently …

We detect out-of-training-distribution sentences in Neural Machine Translation using the
Bayesian Deep Learning equivalent of Transformer models. For this we develop a new …