Post-Editing

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

DivEMT is a publicly available post-editing study of Neural Machine Translation over a typologically diverse set of target languages.