A Case Study on Contextual Machine Translation in a Professional Scenario of Subtitling
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866913409262419968 |
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| author | Vincent, Sebastian Prescott, Charlotte Bayliss, Chris Oakley, Chris Scarton, Carolina |
| author_facet | Vincent, Sebastian Prescott, Charlotte Bayliss, Chris Oakley, Chris Scarton, Carolina |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_00108 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Case Study on Contextual Machine Translation in a Professional Scenario of Subtitling Vincent, Sebastian Prescott, Charlotte Bayliss, Chris Oakley, Chris Scarton, Carolina Machine Learning Artificial Intelligence Computation and Language Human-Computer Interaction 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. |
| title | A Case Study on Contextual Machine Translation in a Professional Scenario of Subtitling |
| topic | Machine Learning Artificial Intelligence Computation and Language Human-Computer Interaction |
| url | https://arxiv.org/abs/2407.00108 |