| Download | - View final version: Metric Score Landscape Challenge (MSLC23): understanding metrics' performance on a wider landscape of translation quality (PDF, 4.6 MiB)
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| DOI | Resolve DOI: https://doi.org/10.18653/v1/2023.wmt-1.65 |
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| Author | Search for: Lo, Chi-kiu1; Search for: Larkin, Samuel1; Search for: Knowles, Rebecca1 |
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| Affiliation | - National Research Council Canada. Digital Technologies
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| Format | Text, Article |
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| Conference | 8th Conference on Machine Translation, WMT 2023, Singapore, December 6-7, 2023, Singapore |
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| Subject | computational linguistics; gain insight; high quality; machine translations; media quality; metric scores; performance; quality systems; test sets; translation quality; machine translation |
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| Abstract | The Metric Score Landscape Challenge (MSLC23) dataset aims to gain insight into metric scores on a broader/wider landscape of machine translation (MT) quality. It provides a collection of low- to medium-quality MT output on the WMT23 general task test set. Together with the high quality systems submitted to the general task, this will enable better interpretation of metric scores across a range of different levels of translation quality. With this wider range of MT quality, we also visualize and analyze metric characteristics beyond just correlation. |
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| Publication date | 2023-12-06 |
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| Publisher | Association for Computational Linguistics |
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| Licence | |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | 8bf1ac87-8c1e-459c-a6f0-b5486dbbbf6b |
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| Record created | 2024-01-08 |
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| Record modified | 2024-01-18 |
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