We present the joint contribution of Unbabel and Instituto Superior Técnico to the WMT 2023 Shared Task on Quality Estimation (QE). Our team participated on all tasks: sentence- and word-level quality prediction (task 1) and fine-grained error span detection (task 2). For all tasks, we build on the COMETKIWI-22 model (Rei et al., 2022b). Our multilingual approaches are ranked first for all tasks, reaching state-of-the-art performance for quality estimation at word-, span- and sentence-level granularity. Compared to the previous state-of-the-art, COMETKIWI-22, we show large improvements in correlation with human judgements (up to 10 Spearman points). Moreover, we surpass the second-best multilingual submission to the shared-task with up to 3.8 absolute points.
CITATION STYLE
Rei, R., Guerreiro, N. M., Pombal, J., van Stigt, D., Treviso, M., Coheur, L., … Martins, A. F. T. (2023). Scaling up COMETKIWI: Unbabel-IST 2023 Submission for the Quality Estimation Shared Task. In Conference on Machine Translation - Proceedings (pp. 839–846). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.wmt-1.73
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