Comparative Analysis of Current Approaches to Quality Estimation for Neural Machine Translation

• Published in: Applied sciences Vol. 11; no. 14; p. 6584
• Main Authors: Eo, Sugyeong, Park, Chanjun, Moon, Hyeonseok, Seo, Jaehyung, Lim, Heuiseok
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.07.2021
• Subjects: Comparative analysis; Data augmentation; Deep learning; Language; Machine translation; multilingual pre-trained language model; Multilingualism; neural machine translation; pretrained language model; quality estimation; Quantitative analysis; Statistical methods; Translation; WMT
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app11146584

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 the Conference on Machine Translation (WMT), and most recent studies have applied the multilingual pretrained language model (mPLM) to address this task. Recent studies have focused on the performance improvement of this task using data augmentation with finetuning based on a large-scale mPLM. In this study, we eliminate the effects of data augmentation and conduct a pure performance comparison between various mPLMs. Separate from the recent performance-driven QE research involved in competitions addressing a shared task, we utilize the comparison for sub-tasks from WMT20 and identify an optimal mPLM. Moreover, we demonstrate QE using the multilingual BART model, which has not yet been utilized, and conduct comparative experiments and analyses with cross-lingual language models (XLMs), multilingual BERT, and XLM-RoBERTa.