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

Cross-lingual machine translation (MT) evaluation or quality estimation is usually performed
either from cross-lingual transfer in supervised tasks or via unsupervised multi-lingual …

Abstract Translation Quality Estimation (QE) is the task of predicting the quality of machine
translation (MT) output without any reference. This task has gained increasing attention as …

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 …

In this paper, we present the contribution of HW-TSC to WMT 2022 Metrics Shared Task. We
propose one reference-based metric, HWTSC-EE-BERTScore*, and four referencefree …

The advent of large language models has experienced a remarkable improvement in the
field of machine translation. However, machine translation is still vulnerable to critical …

This paper describes anonymous submission to the WMT 2022 Quality Estimation shared
task. We participate in Task 1: Quality Prediction for both sentence and word-level quality …

Previous studies show that neural machine translation (NMT) systems produce translations
with higher quality when highly similar sentences (ie fuzzy matches; FMs) to a given input …

Machine Translation Quality Estimation (MTQE) is a growing research topic that aims to
predict human post-editing efforts without relying on references. This can save time and …