Scaling up COMETKIWI: Unbabel-IST 2023 Submission for the Quality
  Estimation Shared Task
Ricardo Rei Nuno M. Guerreiro José Pombal Daan van Stigt Marcos Treviso Luisa Coheur José G. C. de Souza André F. T. Martins
We present the joint contribution of Unbabel and Instituto Superior T\'ecnico
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.