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? |
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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: | EAMT2018 - Proceedings Investigaciones financiadas por la UE |
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