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

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 wh... Mehr ...

Verfasser: Chaïm van Toledo
Marijn Schraagen
Friso van Dijk
Matthieu Brinkhuis
Marco Spruit
Dokumenttyp: Text
Erscheinungsdatum: 2023
Verlag/Hrsg.: Multidisciplinary Digital Publishing Institute
Schlagwörter: quality estimation / SQUAD 2.0 / machine translation / English to Dutch quality estimation
Sprache: Englisch
Permalink: https://search.fid-benelux.de/Record/base-28996654
Datenquelle: BASE; Originalkatalog
Powered By: BASE
Link(s) : https://doi.org/10.3390/app13074444

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.