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.- Anthology ID:
- 2023.wmt-1.65
- Volume:
- Proceedings of the Eighth Conference on Machine Translation
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Philipp Koehn, Barry Haddow, Tom Kocmi, Christof Monz
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 776–799
- Language:
- URL:
- https://aclanthology.org/2023.wmt-1.65
- DOI:
- 10.18653/v1/2023.wmt-1.65
- Cite (ACL):
- Chi-kiu Lo, Samuel Larkin, and Rebecca Knowles. 2023. Metric Score Landscape Challenge (MSLC23): Understanding Metrics’ Performance on a Wider Landscape of Translation Quality. In Proceedings of the Eighth Conference on Machine Translation, pages 776–799, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- Metric Score Landscape Challenge (MSLC23): Understanding Metrics’ Performance on a Wider Landscape of Translation Quality (Lo et al., WMT 2023)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2023.wmt-1.65.pdf