Quality of Post-Edited Interlingual Subtitling: FAR Model, Translator’s Assessment and Audience Reception

Abstract

This paper analyzes the quality of machine translated interlingual subtitles, which were post-edited in the language pair EN/PT-BR. Our analyses applied the FAR model, a Translation Quality Assessment Model, to the PT-BR subtitles of The Red Sea Diving Resort movie trailer, correlating it to empirical data collected with translators (quality assessment) and audience (reception). Reception data was collected with undergraduate students, which were divided into two groups: the control group that watched the subtitled trailer available on Netflix; and the experimental group that watched the trailer with post-edited subtitles. Quality data was collected with translators and they watched the trailer with post-edited subtitles. We used a 5-point Likert-type scale, a questionnaire and a guided think-aloud protocol to collect our data. Data collected with both translators and students were correlated with the FAR model error scores (Functional Equivalence, Acceptability and Readability). Our results indicate that the post-edited subtitles had good quality in terms of meaning and target language norms; however, the technical parameters had lower quality, which affected the trailer appreciation as reported by most of the audience. Due to the small sample size, further empirical studies are required to obtain solid standards for assessing the quality of post-edited subtitles.

Keywords

• Subtitling Quality Assessment
• Reception
• Machine Translation
• Post-Editing
• FAR Model