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

arXiv: https://arxiv.org/abs/2407.00108 · HTML: https://mlsanity.com/paper/2407.00108/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 15 references that are in the map.
- First posted on arXiv 27 Jun 2024.

## Builds on (most cited first, 5 of 5)

- [COMET: A Neural Framework for MT Evaluation](https://mlsanity.com/paper/2009.09025/) (2020)
- [Experts, Errors, and Context: A Large-Scale Study of Human Evaluation for Machine Translation](https://mlsanity.com/paper/2104.14478/) (2021)
- [Neural Machine Translation with Extended Context](https://mlsanity.com/paper/1708.05943/) (2017)
- [Evaluating Discourse Phenomena in Neural Machine Translation](https://mlsanity.com/paper/1711.00513/) (2017)
- [Problems with automating translation of movie/TV show subtitles](https://mlsanity.com/paper/1909.05362/) (2019)

## Built on by (most cited first, 2 of 2)

- [Enhancing Entertainment Translation for Indian Languages using Adaptive Context, Style and LLMs](https://mlsanity.com/paper/2412.20440/) (2024)
- [Workload-Driven Optimization for On-Device Real-Time Subtitle Translation](https://mlsanity.com/paper/2607.09957/) (2026)

## Abstract (arXiv)

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.