Castilho, Sheila, Guerberof Arenas, Ana
Reading Comprehension of Machine Translation Output: What Makes for a Better Read?
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
URI: http://hdl.handle.net/10045/76032
DOI: 
ISSN: 
ISBN: 978-84-09-01901-4
Abstract: 
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.
Keywords:Machine Translation
European Association for Machine Translation
info:eu-repo/semantics/conferenceObject