Readability Metrics for Machine Translation in Dutch: Google vs. Azure & IBM

Chaïm van Toledo*, Marijn Schraagen, Friso van Dijk, Matthieu Brinkhuis, Marco Spruit

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

This paper introduces a novel method to predict when a Google translation is better than other machine translations (MT) in Dutch. Instead of considering fidelity, this approach considers fluency and readability indicators for when Google ranked best. This research explores an alternative approach in the field of quality estimation. The paper contributes by publishing a dataset with sentences from English to Dutch, with human-made classifications on a best-worst scale. Logistic regression shows a correlation between T-Scan output, such as readability measurements like lemma frequencies, and when Google translation was better than Azure and IBM. The last part of the results section shows the prediction possibilities. First by logistic regression and second by a generated automated machine learning model. Respectively, they have an accuracy of 0.59 and 0.61.
Original languageEnglish
Article number4444
Pages (from-to)1-14
Number of pages14
JournalApplied Sciences
Volume13
Issue number7
DOIs
Publication statusPublished - 1 Apr 2023

Keywords

  • English to Dutch quality estimation
  • Machine translation
  • Quality estimation
  • Squad 2.0

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