A Case Study on Contextual Machine Translation in a Professional Scenario of Subtitling

TL;DR

This study investigates the impact of incorporating extra-textual context into machine translation for TV subtitles, demonstrating reduced errors and highlighting the importance of context in professional translation workflows.

Contribution

It provides an industrial case study showing that context-aware MT reduces errors in subtitle translation and emphasizes the need for fully contextual MT systems.

Findings

Post-editors made fewer errors with context-aware MT

Contextual inadequacy is a significant gap in current MT systems

Survey highlights the importance of context for translation quality

Abstract

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.