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MT syntactic priming effects on L2 English speakers

Resende, Natália orcid logoORCID: 0000-0002-5248-2457, Cowan, Benjamin orcid logoORCID: 0000-0002-8595-8132 and Way, Andy orcid logoORCID: 0000-0001-5736-5930 (2020) MT syntactic priming effects on L2 English speakers. In: European Association for Machine Translation (EAMT) 2020, 2-6 Nov 2010, Lisbon, Portugal (Online). ISBN 978-989-33-0589-8

Abstract
In this paper, we tested 20 Brazilian Portuguese speakers at intermediate and ad- vanced English proficiency levels to inves- tigate the influence of Google Translate’s MT system on the mental processing of English as a second language. To this end, we employed a syntactic priming experi- mental paradigm using a pretest-priming design which allowed us to compare par- ticipants’ linguistic behaviour before and after a translation task using Google Trans- late. Results show that, after performing a translation task with Google Translate, participants more frequently described im- ages in English using the syntactic alter- native previously seen in the output of Google Translate, compared to the transla- tion task with no prior influence of the MT output. Results also show that this syntac- tic priming effect is modulated by English proficiency levels.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:Portuguese; Language Learning; Syntactic priming; Syntactic alignment; Translation; Human-Computer interaction
Subjects:Computer Science > Artificial intelligence
Computer Science > Machine translating
Humanities > Language
Humanities > Linguistics
Humanities > Translating and interpreting
Medical Sciences > Psychology
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Institutes and Centres > ADAPT
Published in: Proceedings of the 22nd Annual Conference of the European Association for Machine Translation. . European Association for Machine Translation (EAMT). ISBN 978-989-33-0589-8
Publisher:European Association for Machine Translation (EAMT)
Official URL:https://www.aclweb.org/anthology/2020.eamt-1.26
Copyright Information:© 2020 The Authors. CC-BY-4.0
Funders:Science Foundation Ireland Research Centres Programme (Grant 13/RC/2106) and is co-funded by the European Regional Development Fund. T, European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant no. 843455.
ID Code:24989
Deposited On:08 Sep 2020 14:50 by Natalia Resende . Last Modified 10 Mar 2021 12:20
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