Reading comprehension of machine translation output: what makes for a better read?

• Autores: Sheila Castilho, Ana Guerberof Arenas
• Localización: Proceedings of the 21st Annual Conference of the European Association for Machine Translation: 28-30 May 2018, Universitat d'Alacant, Alacant, Spain / coord. por Juan Antonio Pérez Ortiz, Felipe Sánchez Martínez, Miquel Esplà Gomis, Maja Popovic, Celia Rico Pérez, André Martins, Joachim Van den Bogaert, Mikel L. Forcada Zubizarreta, 2018, ISBN 978-84-09-01901-4, págs. 79-88
• Idioma: inglés
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• 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.