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: Toledo, Chaïm van
Schraagen, Marijn
Dijk, Friso van
Brinkhuis, Matthieu
Spruit, Marco
Dokumenttyp: Artikel
Erscheinungsdatum: 2023
Schlagwörter: English to Dutch quality estimation / Machine translation / Quality estimation / Squad 2.0
Sprache: Englisch
Permalink: https://search.fid-benelux.de/Record/base-29040019
Datenquelle: BASE; Originalkatalog
Powered By: BASE
Link(s) : https://dspace.library.uu.nl/handle/1874/427678

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.