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 …

Quality estimation (QE) aims to assess the quality of machine translations when reference
translations are unavailable. QE plays a crucial role in many real-world applications of …

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 …

Quality estimation (QE) is a crucial method to investigate automatic methods for estimating
the quality of machine translation results without reference translations. This paper presents …

This paper presents our work in WMT 2021 Quality Estimation (QE) Shared Task. We
participated in all of the three sub-tasks, including Sentence-Level Direct Assessment (DA) …

Quality estimation (QE) has recently gained increasing interest as it can predict the quality of
machine translation results without a reference translation. QE is an annual shared task at …

This paper presents KU X Upstage's submission to the quality estimation (QE): critical error
detection (CED) shared task in WMT22. We leverage the XLM-RoBERTa large model …

Abstract Quality Estimation (QE) for Machine Translation has been shown to reach relatively
high accuracy in predicting sentence-level scores, relying on pretrained contextual …

Quality estimation models have been developed to assess the corrections made by
grammatical error correction (GEC) models when the reference or gold-standard corrections …

This paper proposes a tool for efficiently constructing high-quality parallel corpora with
minimizing human labor and making this tool publicly available. Our proposed construction …