Reading Comprehension of Machine Translation Output: What Makes for a Better Read?

Please use this identifier to cite or link to this item: http://hdl.handle.net/10045/76032
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dc.contributor.authorCastilho, Sheila-
dc.contributor.authorGuerberof Arenas, Ana-
dc.date.accessioned2018-05-30T12:13:21Z-
dc.date.available2018-05-30T12:13:21Z-
dc.date.issued2018-
dc.identifier.citationCastilho, Sheila; Guerberof Arenas, Ana. “Reading Comprehension of Machine Translation Output: What Makes for a Better Read?”. In: Pérez-Ortiz, Juan Antonio, et al. (Eds.). Proceedings of the 21st Annual Conference of the European Association for Machine Translation: 28-30 May 2018, Universitat d'Alacant, Alacant, Spain, pp. 79-88es_ES
dc.identifier.isbn978-84-09-01901-4-
dc.identifier.urihttp://hdl.handle.net/10045/76032-
dc.description.abstractThis paper reports on a pilot experiment that compares two different machine translation (MT) paradigms in reading comprehension tests. To explore a suitable methodology, we set up a pilot experiment with a group of six users (with English, Spanish and Simplified Chinese languages) using an English Language Testing System (IELTS), and an eye-tracker. The users were asked to read three texts in their native language: either the original English text (for the English speakers) or the machine-translated text (for the Spanish and Simplified Chinese speakers). The original texts were machine-translated via two MT systems: neural (NMT) and statistical (SMT). The users were also asked to rank satisfaction statements on a 3-point scale after reading each text and answering the respective comprehension questions. After all tasks were completed, a post-task retrospective interview took place to gather qualitative data. The findings suggest that the users from the target languages completed more tasks in less time with a higher level of satisfaction when using translations from the NMT system.es_ES
dc.description.sponsorshipThis research was supported by the Edge Research Fellowship programme that has received funding from the European Unions Horizon 2020 and innovation programme under the Marie Sklodowska-Curie grant agreement No. 713567, and by the 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.es_ES
dc.languageenges_ES
dc.publisherEuropean Association for Machine Translationes_ES
dc.rights© 2018 The authors. This article is licensed under a Creative Commons 3.0 licence, no derivative works, attribution, CC-BY-ND.es_ES
dc.subjectMachine Translationes_ES
dc.subject.otherLenguajes y Sistemas Informáticoses_ES
dc.titleReading Comprehension of Machine Translation Output: What Makes for a Better Read?es_ES
dc.typeinfo:eu-repo/semantics/conferenceObjectes_ES
dc.peerreviewedsies_ES
dc.relation.publisherversionhttp://eamt2018.dlsi.ua.es/proceedings-eamt2018.pdfes_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/713567es_ES
Appears in Collections:Congresos - EAMT2018 - Proceedings
Research funded by the EU

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