Audiovisual Translation Through NMT and Subtitling in the Netflix Series Summertime


Over the past few years, the rise of Netflix, HBO, Amazon Prime Video and other streaming platforms has made it necessary to rethink entertainment media. Accessibility to their catalogues not only offers the audience the opportunity to choose among a variety of films, series, documentaries and other audiovisual resources but also to make use of subtitling and dubbing options (Oh & Noh 2021; Díaz Cintas 2008). Machine Translation (MT) is widely used in the translation industry because the texts tend to be repetitive, and studies have shown that it increases translators’ productivity (Sanchez-Torron 2016) by post-editing the MT output. However, despite the fact that platforms like Netflix announced that they are using MTPE in their subtitling workflows three years ago, research on this topic is still scarce in creative fields, such as literary or audiovisual texts. This ongoing project aims to ascertain the quality of Google Translate and DeepL translations (i.e. open MT resources) when compared to the subtitling of TV series in the source language. On this account, the current study draws from the following research questions: RQ(1) How do English subtitling and translations from NMT differ from the source text in Italian? What types of errors can be found? and RQ(2) Does the integration of MT on the audiovisual translation workflow benefit translators? The corpus under study revolves around the Italian Netflix original series called Summertime, a modern love story set on the Adriatic coast that premiered in 2020.

Author Information
Giulia Magazzù, “Gabriele d’Annunzio” University of Chieti-Pescara, Italy

Paper Information
Conference: ECAH2023
Stream: Language

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Posted by James Alexander Gordon