Quality Estimation

This dissertation bridges the gap between scientific insights into how language models work and practical benefits for users of these systems, paving the way for better human-AI interaction practices for professional translators and everyday users worldwide.

We evaluate unsupervised word-level quality estimation (WQE) methods for machine translation, focusing on their robustness to human label variation.

We investigate the impact of word-level quality estimation on MT post-editing with 42 professional post-editors.