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We present the joint contribution of Unbabel and Instituto Superior T\'ecnicoto the WMT 2023 Shared Task on Quality Estimation (QE). Our team participatedon all tasks: sentence- and word-level quality prediction (task 1) andfine-grained error span detection (task 2). For all tasks, we build on theCOMETKIWI-22 model (Rei et al., 2022b). Our multilingual approaches are rankedfirst for all tasks, reaching state-of-the-art performance for qualityestimation at word-, span- and sentence-level granularity. Compared to theprevious state-of-the-art COMETKIWI-22, we show large improvements incorrelation with human judgements (up to 10 Spearman points). Moreover, wesurpass the second-best multilingual submission to the shared-task with up to3.8 absolute points.

Scaling up COMETKIWI: Unbabel-IST 2023 Submission for the Quality Estimation Shared Task