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What is the impact of raw MT on Japanese users of Word preliminary results of a usability study using eye-tracking

Guerberof Arenas, Ana orcid logoORCID: 0000-0001-9820-7074, Moorkens, Joss orcid logoORCID: 0000-0003-4864-5986 and O'Brien, Sharon orcid logoORCID: 0000-0003-4864-5986 (2019) What is the impact of raw MT on Japanese users of Word preliminary results of a usability study using eye-tracking. In: XVII Machine Translation Summit, 19-23 Aug 2019, Dublin, Ireland.

Abstract
This paper presents preliminary results of a study of Japanese native speakers working with the Microsoft Word application in two modalities: the released Japanese version and a machine translated (MT) version (the raw MT strings incorporated into the MS Word interface). To explore the effect of translation modality on task completion, time and satisfaction, an experiment using an eye-tracker was set up with a group of 42 users: 22 native Japanese and 20 native English speakers. The results suggest that Japanese-native speakers have higher completion scores and are more efficient when working with the released versions of the product than with the MT version, but these differences are not significant. Their self-reported satisfaction, however, is significantly higher when working with the released product as opposed to the raw MT version.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:usability; satisfaction; effectiveness; efficiency; eye-tracking
Subjects:Computer Science > Computational linguistics
Computer Science > Machine translating
Humanities > Japanese language
Humanities > Language
Humanities > Translating and interpreting
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Humanities and Social Science > School of Applied Language and Intercultural Studies
Research Institutes and Centres > ADAPT
Published in: Proceedings of the XVII Machine Translation Summit: Research Track. 1. European Association of Machine Translation.
Publisher:European Association of Machine Translation
Official URL:https://www.aclweb.org/anthology/W19-6607
Copyright Information:© 2019 The Authors. (CC-BY-ND 4.0)
Funders:Edge Research Fellowship programme that has received funding from the EU Horizon 2020 and innovation programme under the MSC grant agreement No. 713567, ADAPT Centre for Digital Content Technology, funded under the SFI Research Centres Programme (Grant 13/RC/2106) and co-funded under the European Regional Development Fund, Microsoft Ireland.
ID Code:23641
Deposited On:19 Aug 2019 10:42 by Ana Guerberof . Last Modified 03 Aug 2022 15:44
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