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

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Título: Reading Comprehension of Machine Translation Output: What Makes for a Better Read?
Autor/es: Castilho, Sheila | Guerberof Arenas, Ana
Palabras clave: Machine Translation
Área/s de conocimiento: Lenguajes y Sistemas Informáticos
Fecha de publicación: 2018
Editor: European Association for Machine Translation
Cita bibliográfica: Castilho, 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. 79-88
Resumen: This 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.
Patrocinador/es: This 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.
URI: http://hdl.handle.net/10045/76032
ISBN: 978-84-09-01901-4
Idioma: eng
Tipo: info:eu-repo/semantics/conferenceObject
Derechos: © 2018 The authors. This article is licensed under a Creative Commons 3.0 licence, no derivative works, attribution, CC-BY-ND.
Revisión científica: si
Versión del editor: http://eamt2018.dlsi.ua.es/proceedings-eamt2018.pdf
Aparece en las colecciones:Congresos - EAMT2018 - Proceedings
Investigaciones financiadas por la UE

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