[GALA Connected 2021: Bounce Forward] Science Fiction or Reality Using MT for Media Translations

07 Oct 2021

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Only a few years ago using machine translation for subtitles, especially in the entertainment area, seemed completely unrealistic. The very nature of subtitles and oral speech seemed to make it impossible: sentences split into subtitle lines, interjections, character count and reading speed limitations are only some of the challenges that a typical MT engine wouldn’t be able to handle, and in the best-case scenario produce an unusable result.

Today, neural MT changed the way we think about it, and many companies, including very famous streaming platforms, are successfully applying MT for subtitles. But are all these problems solved?

In this talk Anna Zaretskaya presents a study carried out with the aim to identify the current limitations of NMT engines when applied to subtitles. Using content from several different TV shows she analyzed the types of errors produced by NMT engines. The goal was to understand the level of quality produced by state-of-the-art MT and whether it is actually helps increase translation efficiency. In addition, this analysis helped identify areas on which we need to focus our improvement efforts. Finally, by covering three different language combinations she reflects on which languages pose more challenges in this context.

This presentation is of interest to anyone who works in the field of media translations or is interested in MT and its current limitations. Viewers gain insight into how much MT can really drive efficiency gains in media translation workflows, main factors it depends on, such as language pair, type of content, among others, and find out what is needed to make such setup successful.

Host organization: TransPerfect

Event Speakers

Anna Zaretskaya
TransPerfect

Anna Zaretskaya joined TransPerfect in 2016 as a Machine Translation Training Coordinator; after finishing her PhD in translation technologies and user needs. She has a background in general linguistics; (undergraduate studies) and computational linguistics (MS). At first, her role at TransPerfect consisted in establishing and maintaining relationships with freelance translators and educating them on MT and post-editing, as well as providing training to internal teams in the company.