Jannis Vamvas

Machine translation benchmarks have functionally saturated, but pairing human-authored failure cases with deterministic verification rules reveals critical multimodal blind spots that automated metrics consistently miss.

RL-trained models can significantly improve unseen language translation by effectively leveraging contextual linguistic knowledge, outperforming traditional methods.

A single lemmatizer now handles five Romansh varieties plus the standard form, achieving high accuracy in both lemmatization and dialect identification.