Year: 2023
Type: conference-paper
Abstract: 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: sentenceand 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.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4389519402