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

Authors :
Eo, Sugyeong
Park, Chanjun
Moon, Hyeonseok
Seo, Jaehyung
Lim, Heuiseok
Source :
Applied Sciences (2076-3417); Jul2021, Vol. 11 Issue 14, p6584, 15p
Publication Year :
2021

Abstract

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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20763417
Volume :
11
Issue :
14
Database :
Complementary Index
Journal :
Applied Sciences (2076-3417)
Publication Type :
Academic Journal
Accession number :
151562095
Full Text :
https://doi.org/10.3390/app11146584