Problems with automating translation of movie/TV show subtitles

Abstract

We present 27 problems encountered in automating the translation of movie/TV show subtitles. We categorize each problem in one of the three categories viz. problems directly related to textual translation, problems related to subtitle creation guidelines, and problems due to adaptability of machine translation (MT) engines. We also present the findings of a translation quality evaluation experiment where we share the frequency of 16 key problems. We show that the systems working at the frontiers of Natural Language Processing do not perform well for subtitles and require some post-processing solutions for redressal of these problems.

Integrating post-editing into the subtitling classroom: what do subtitlers-to-be think?

“…Punctuation and capitalization obtained a higher score but still presented problems (μ=2.86 and μ=2.45, respectively), which may be due to the fact that NMT engines typically find it challenging to follow subtitling-specific punctuation conventions. Indeed, NMT developers often neglect these conventions, as has been observed in studies such as that conducted by Gupta et al (2019). Remarkably, the AT template performed best at spelling (μ=1.69 out of 5; σ=0.967) and was perceived to be more faithful than the HT to the English original dialogue contained in the template (i.e., omissions and additions never or rarely occurred, with μ=1.9 and μ=1.62, respectively).…”