Is 42 the Answer to Everything in Subtitling-oriented Speech Translation? Alina Karakanta Matteo Negri Marco Turchi Fondazione Bruno Kessler Fondazione Bruno Kessler Fondazione Bruno Kessler University of Trento Trento - Italy Trento - Italy Trento - Italy
[email protected] [email protected] [email protected] Abstract content, still rely heavily on human effort. In a typi- cal multilingual subtitling workflow, a subtitler first Subtitling is becoming increasingly important creates a subtitle template (Georgakopoulou, 2019) for disseminating information, given the enor- by transcribing the source language audio, timing mous amounts of audiovisual content becom- and adapting the text to create proper subtitles in ing available daily. Although Neural Machine Translation (NMT) can speed up the process the source language. These source language subti- of translating audiovisual content, large man- tles (also called captions) are already compressed ual effort is still required for transcribing the and segmented to respect the subtitling constraints source language, and for spotting and seg- of length, reading speed and proper segmentation menting the text into proper subtitles. Cre- (Cintas and Remael, 2007; Karakanta et al., 2019). ating proper subtitles in terms of timing and In this way, the work of an NMT system is already segmentation highly depends on information simplified, since it only needs to translate match- present in the audio (utterance duration, natu- ing the length of the source text (Matusov et al., ral pauses). In this work, we explore two meth- ods for applying Speech Translation (ST) to 2019; Lakew et al., 2019). However, the essence subtitling: a) a direct end-to-end and b) a clas- of a good subtitle goes beyond matching a prede- sical cascade approach.