#post-editing

[chaddiction]

Carlo After Dark

@kalooooyskiiii

[griffelschwinger]

KI in der Buchbranche: Das unmoralische Angebot von Bastei Lübbe

Im vergangenen Jahr machte die Übersetzerin Janine Malz öffentlich, dass ein Verlag sie nur dafür anheuern wollte, eine KI-generierte Überse

[paulfilkin]

AdaptiveMT... what's the score?

AdaptiveMT… what’s the score?

AdaptiveMT was released with Studio 2017 introducing the ability for users to adapt the SDL Language Cloud machine translation with their own preferred style on the fly.  Potentially this is a really powerful feature since it means that over time you should be able to improve the results you see from your SDL Language Cloud machine translation and reduce the amount of post editing you have to do.…

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[chatzikoumi]

Los productos de la traducción automática (TA), aplicación presente en todo tipo de dispositivos y para todo tipo de usuarios, generan una gama amplia de reacciones en los traductores, desde el horror hasta la risa, a pesar de las cuales o, tal vez,

[paulfilkin]

Spot the difference!

I don’t know if you can recall these games from when you were a kid?  I used to spend hours trying to find all the differences between the image on the left and the one on the right.  I never once thought how that might become a useful skill in later life… although in some cases it’s a skill I’d rather not have to develop!

You may be wondering where I’m going with this so I’ll explain.  Last…

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[chatzikoumi]

Machine Translation Quality: Automatic and Human Evaluation of four MT systems' output

Master's Thesis | Master's Programme in Language Technology | UOA, NTUA, ILSP

Eirini Chatzikoumi, 2015

By evaluating the outputs of four MT systems in the language pair English-Greek in both directions, we aim to assess their output quality, draw conclusions on it and suggest ways of exploiting these conclusions. For this evaluation project we use the Bleu metric as well as human evaluation methods. As three of the systems are commercial while the fourth one is under development, the methodology of their human evaluation is adjusted to the different needs, thus for the commercial systems it focuses on their ranking, while for the prototype it focuses on its improvement. The Bleu metric is also indirectly evaluated, as its correlation with human judgment is studied.

The corpus built for the needs of our evaluation project comprises 30 source texts, as well as 3 reference translations and 4 machine translations for each one of them. Half of the source texts are English and the other half are Greek, and in each direction we study 5 texts from each one of the following fields: medicine, law & administration and technology. The total number of segments of the English texts is 1,726 and of the Greek texts 1,103. Upon observation of the Bleu scores, a small number of texts is selected for human evaluation. The human evaluation tasks performed are Quality Checking, 3-way Ranking, Error Classification and Post-editing. As a result of the post-editing task, a by-product of this evaluation is a corpus of 331 post-edited segments (229 English-Greek and 102 Greek-English), the total number of edits being 2,158 (1,434 English-Greek and 724 Greek-English). Our conclusions regard the four systems' ranking, the performance of each system in the three above-mentioned fields and the relation between sentence length and translation quality. Finally, the prototype's outputs submitted to error classification provide some interesting insight on the most prominent error types in each translation direction in the given language pair.

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[personal-interestique]

My first panorama edited on GIMP. Not perfect, but not a bad first try I think ;)