Computational analysis of different translations: by professionals, students and machines

Maja Popovic, Ekaterina Lapshinova-Koltunski, Maarit Koponen


Abstract
In this work, we analyse different translated texts in terms of various text features. We compare two types of human translations, professional and students’, and machine translation outputs in terms of lexical and grammatical variety, sentence length,as well as frequencies of different POS tags and POS-trigrams. Our experimentsare carried out on parallel translations into three languages, Croatian, Finnish andRussian, all originating from the same source English texts. Our results indicatethat machine translations are closest to the source text, followed by student translations. Also, student translations are similar both to professional as well as to MT, sometimes even more to MT. Furthermore, we identify sets of features which are convenient for distinguishing machine from human translations.
Anthology ID:
2023.eamt-1.36
Volume:
Proceedings of the 24th Annual Conference of the European Association for Machine Translation
Month:
June
Year:
2023
Address:
Tampere, Finland
Editors:
Mary Nurminen, Judith Brenner, Maarit Koponen, Sirkku Latomaa, Mikhail Mikhailov, Frederike Schierl, Tharindu Ranasinghe, Eva Vanmassenhove, Sergi Alvarez Vidal, Nora Aranberri, Mara Nunziatini, Carla Parra Escartín, Mikel Forcada, Maja Popovic, Carolina Scarton, Helena Moniz
Venue:
EAMT
SIG:
Publisher:
European Association for Machine Translation
Note:
Pages:
365–374
Language:
URL:
https://aclanthology.org/2023.eamt-1.36
DOI:
Bibkey:
Cite (ACL):
Maja Popovic, Ekaterina Lapshinova-Koltunski, and Maarit Koponen. 2023. Computational analysis of different translations: by professionals, students and machines. In Proceedings of the 24th Annual Conference of the European Association for Machine Translation, pages 365–374, Tampere, Finland. European Association for Machine Translation.
Cite (Informal):
Computational analysis of different translations: by professionals, students and machines (Popovic et al., EAMT 2023)
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PDF:
https://aclanthology.org/2023.eamt-1.36.pdf