2021/09/08 by Shaika Chowdhury, Chowdhury, Shaika, Naouel Baili +3 · 2 citations
Computer Science · Mathematics · #Artificial intelligence #Baseline (sea) #Component (thermodynamics) #Computer science #Machine translation #Mathematics #Natural Language Processing Techniques #Natural language processing #Quality (philosophy) #Regression #Sentence #Software Engineering Research #Speech recognition #Statistics #Task (project management) #Topic Modeling #Translation (biology) #Workflow #cs.CL
paper · pdf · doi:10.48550/arxiv.2109.03914
published in arXiv (Cornell University) (Cornell University) · The Sixth Conference on Machine Translation, WMT 2021
arxiv created 2021/09/08 · openalex publication_date 2021/09/08 · arxiv updated 2021/09/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Quality Estimation (QE) is an important component of the machine translation workflow as it assesses the quality of the translated output without consulting reference translations. In this paper, we discuss our submission to the WMT 2021 QE Shared Task. We participate in Task 2 sentence-level sub-task that challenge participants to predict the HTER score for sentence-level post-editing effort. Our proposed system is an ensemble of multilingual BERT (mBERT)-based regression models, which are generated by fine-tuning on different input settings. It demonstrates comparable performance with respect to the Pearson's correlation and beats the baseline system in MAE/ RMSE for several language pairs. In addition, we adapt our system for the zero-shot setting by exploiting target language-relevant language pairs and pseudo-reference translations.