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

Popović, Maja orcid logoORCID: 0000-0001-8234-8745, Lapshinova-Koltunski, Ekaterina orcid logoORCID: 0000-0002-5618-8087 and Koponen, Maarit orcid logoORCID: 0000-0002-6123-5386 (2023) Computational analysis of different translations: by professionals, students and machines. In: EAMT 2023, 12-15 Jun 2023, Tampere, Finland. ISBN 978-952-03-2947-1

Abstract
In this work, we analyse translated texts in terms of various features. We compare two types of human translations, professional and students’, and machine translation (MT) outputs in terms of lexical and grammatical variety, sentence length, as well as frequencies of different part-of-speech (POS) tags and POS-trigrams. Our analyses are carried out on parallel translations into Croatian, Finnish and Russian, all originating from the same source English texts. Our results indicate that machine translations are the closest to the source text, followed by student translations. Also, student translations are sometimes more similar to MT than to professional translations. Furthermore, we identify sets of features distinctive for machine translations.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Subjects:Computer Science > Machine translating
Humanities > Linguistics
Humanities > Translating and interpreting
DCU Faculties and Centres:Research Initiatives and Centres > ADAPT
Published in: 24th Annual Conference of the European Association of Machine Translation 2022 (EAMT 2023), Proceedings. . European Association for Machine Translation (EAMT). ISBN 978-952-03-2947-1
Publisher:European Association for Machine Translation (EAMT)
Official URL:https://events.tuni.fi/uploads/2023/06/a52469c0-pr...
Copyright Information:© 2023 The Authors.
Funders:EAMTsponsorshipprogramme for2021andbyScience Foundation Ireland under Grant Agreement No.13/RC/2106_P2at the ADAPT SFI Research Centre at Dublin City University., ADAPT, the SFI Research Centre for AI Driven Digital Content Technology, is funded by Science Foundation Ireland through the SFI Research Centres Programme., Kopios to grant awarded by the Finnish Association of Translators and Interpreters.
ID Code:28688
Deposited On:12 Jul 2023 09:30 by Maja Popovic . Last Modified 08 Mar 2024 12:22
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