Automatic post-editing (APE) research considers methods for correcting translation results
inferred by machine translation systems. The training of APE models, generally require …

Data augmentation (DA) is a popular strategy to boost performance on neural machine
translation tasks. The impact of data augmentation in low-resource environments …

Quality estimation (QE) task aims to predict the machine translation (MT) quality well by
referring to the source sentence and its MT output. The various applicability of QE proves the …

With the recent advance in neural machine translation demonstrating its importance,
research on quality estimation (QE) has been steadily progressing. QE aims to automatically …

Translation quality estimation (TQE) is the task of predicting translation quality without
reference translations. Due to the enormous cost of creating training data for TQE, only a few …

Building of data for quality estimation (QE) training is expensive and requires significant
human labor. In this study, we focus on a data-centric approach while performing QE, and …

기계번역 품질 예측 (Quality Estimation, QE) 은 정답 문장에 대한 참조없이 소스 문장과
기계번역 결과를 통해 기계번역 결과에 대한 품질을 수준별 주석으로 나타내주는 태스크이며 …

Recently, there has been a growing interest in zero-shot cross-lingual transfer, which
leverages cross-lingual language models (CLLMs) to perform downstream tasks that are not …