SalamandraTA at WMT 2026 Terminology Shared Task: Hard Examples Are Better Teachers
Terminology-aware translation asks for more than a correct translation: the output must use the exact terms a glossary prescribes. The standard recipe, fine-tuning on glossary-annotated translation pairs, hides an inefficiency: for most examples the glossary prescribes exactly what the model would have produced anyway, so they teach nothing about following a glossary. We therefore keep only the examples where the model's own translation contradicts the glossary. In a controlled study at fixed data volume, this selection alone raises term accuracy from 78.7% to 89.9%. The filtered data, built by a two-way synthetic pipeline on open models, is part of the instruction-tuning mixture of our p...
| Authors | Xixian Liao, Maite Melero |
|---|---|
| Primary category | cs.CL |
| Published | 9 September 2026 |
| Last updated at source | 10 September 2026 |
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