| Téléchargement | - Voir la version finale : Metric Score Landscape Challenge (MSLC23): understanding metrics' performance on a wider landscape of translation quality (PDF, 4.6 Mio)
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| DOI | Trouver le DOI : https://doi.org/10.18653/v1/2023.wmt-1.65 |
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| Auteur | Rechercher : Lo, Chi-kiu1; Rechercher : Larkin, Samuel1; Rechercher : Knowles, Rebecca1 |
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| Affiliation | - Conseil national de recherches du Canada. Technologies numériques
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| Format | Texte, Article |
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| Conférence | 8th Conference on Machine Translation, WMT 2023, Singapore, December 6-7, 2023, Singapore |
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| Sujet | 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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| Résumé | 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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| Date de publication | 2023-12-06 |
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| Maison d’édition | Association for Computational Linguistics |
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| Licence | |
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| Dans | |
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| Langue | anglais |
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| Publications évaluées par des pairs | Oui |
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| Exporter la notice | Exporter en format RIS |
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| Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
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| Identificateur de l’enregistrement | 8bf1ac87-8c1e-459c-a6f0-b5486dbbbf6b |
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| Enregistrement créé | 2024-01-08 |
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| Enregistrement modifié | 2024-01-18 |
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