FairQE: Multi-Agent Framework for Mitigating Gender Bias in Translation Quality Estimation

arXiv:2604.21420v1 Announce Type: new Abstract: Quality Estimation (QE) aims to assess machine translation quality without reference translations, but recent studies have shown that existing QE models exhibit systematic gender bias. In particular, they tend to favor masculine realizations in gender-ambiguous contexts and may assign higher scores to gender-misaligned translations even when gender is explicitly specified. To address these issues, we propose FairQE, a multi-agent-based, fairness-aw