Summary of A Case Study on Contextual Machine Translation in a Professional Scenario Of Subtitling, by Sebastian Vincent and Charlotte Prescott and Chris Bayliss and Chris Oakley and Carolina Scarton
A Case Study on Contextual Machine Translation in a Professional Scenario of Subtitling
by Sebastian Vincent, Charlotte Prescott, Chris Bayliss, Chris Oakley, Carolina Scarton
First submitted to arxiv on: 27 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Human-Computer Interaction (cs.HC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The abstract discusses the potential benefits of incorporating extra-textual context into machine translation (MT) pipelines to enhance translation quality. Recent research has shown that automatic evaluation metrics indicate improved performance when using film metadata, but the impact in industry settings remains unclear. The authors report on an industrial case study investigating MT’s effectiveness in translating TV subtitles, focusing on how leveraging extra-textual context affects post-editing. They found that post-editors marked fewer errors when correcting outputs from a context-aware model compared to non-contextual models. Additionally, a survey of employed post-editors highlights contextual inadequacy as a significant gap consistently observed in MT, motivating further research into fully contextual MT. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning researchers are working on ways to improve translation quality by using extra information like film titles or director names. They want to know if this helps in real-world situations too. In a study, they translated TV subtitles and asked people who edit translations for a living what they thought. The results showed that when they used a special machine learning model that included extra context, the editors found fewer mistakes. This makes them think we should keep working on making machine translation more contextual. |
Keywords
» Artificial intelligence » Machine learning » Translation
