Research
NLP
Are We Estimating or Guesstimating Translation Quality?
July 6, 2020
Abstract
Recent advances in pre-trained multilingual language models lead to state-of-the-art results on the task of quality estimation (QE) for machine translation. A carefully engineered ensemble of such models dominated the QE shared task at WMT 2019. Our in-depth analysis, however, shows that the success of using pre-trained language models for QE is overestimated due to three issues we observed in current QE datasets: (i) The distributions of quality scores are imbalanced and skewed towards good quality scores; (ii) QE models can perform well on these datasets without even ingesting source or translated sentences; (iii) They contain statistical artifacts that correlate well with human-annotated QE labels. Our findings suggest that though QE models might capture fluency of translated sentences and complexity of source sentences, they cannot model adequacy of translations effectively.
AUTHORS
Publisher
Association for Computational Linguistics (ACL)
Research Topics
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