  Incorporating extra-textual context such as film metadata into the machine
translation (MT) pipeline can enhance translation quality, as indicated by
automatic evaluation in recent work. However, the positive impact of such
systems in industry remains unproven. We report on an industrial case study
carried out to investigate the benefit of MT in a professional scenario of
translating TV subtitles with a focus on how leveraging extra-textual context
impacts post-editing. We found that post-editors marked significantly fewer
context-related errors when correcting the outputs of MTCue, the context-aware
model, as opposed to non-contextual models. We also present the results of a
survey of the employed post-editors, which highlights contextual inadequacy as
a significant gap consistently observed in MT. Our findings strengthen the
motivation for further work within fully contextual MT.
