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

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

Applied sciences, 2023-03, Vol.13 (7), p.4444 [Periódico revisado por pares]

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Título:
Readability Metrics for Machine Translation in Dutch: Google vs. Azure & IBM
Autor: van Toledo, Chaïm ; Schraagen, Marijn ; van Dijk, Friso ; Brinkhuis, Matthieu ; Spruit, Marco
Assuntos: Computer industry ; Computer software industry ; Dutch language ; English to Dutch quality estimation ; Language ; Machine learning ; Machine translation ; quality estimation ; Rankings ; Regression analysis ; Sentences ; SQUAD 2.0 ; Translating and interpreting ; Translation ; Translations
É parte de: Applied sciences, 2023-03, Vol.13 (7), p.4444
Descrição: 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.
Editor: Basel: MDPI AG
Idioma: Inglês

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