Controllable machine translation improves low resource language accuracy
GLOSS-MT: Controllable Low-Resource Machine Translation with an Editable Dictionary
Computation and LanguageArtificial Intelligence
Summary
Translating languages that have little data available is hard, especially when you want to use specific words correctly. This paper shows a way to use a dictionary to replace important words in a sentence before translating, so the translation keeps those words right without much extra work. The authors tested this on English to Asante Twi translation and found it worked better than before, accurately using the dictionary words. Their method lets people change dictionary entries to control translations without retraining the model.
What this means in practice
- •For machine translation engineers: Improve low-resource language translation by integrating an editable dictionary to control terminology with minimal fine-tuning.
- •For localization teams: Customize translations to preferred terminology rapidly by swapping dictionary entries without needing additional model retraining.
Authors
Mich-Seth Owusu, Stephen E. Moore, Akwasi Asare, Lawrence Adu Gyamfi, Paul Azunre, Joel Budu, Jonathan Asiamah
Abstract
Adapting a pretrained machine translation (MT) model to new terminology usually requires new parallel data and another round of fine-tuning, which is costly for low-resource languages. We ask whether controllability can be added to MT, and the fine-tuning burden reduced, without degrading the pretrained model. GLOSS-MT looks up the source's noun phrases in an offline English-Twi dictionary, replaces every phrase found by its Twi translation in place, and fine-tunes a translation model once to translate these mixed inputs. We apply the same data and pipeline to NLLB-200 (600M, 1.3B, 3.3B), OPUS-MT en-tw and TranslateGemma 4B. On a document-level English-to-Asante Twi benchmark (84 documents, four domains), every fine-tune scores above its pretrained base in chrF2 (+2.85 to +5.48), and the three NLLB fine-tunes are the best open systems (50.12-50.80). A direct controllability test on human-translated Bible passages shows that the three fine-tunes tested write 79-97% of the dictionary terms, and that replacing every dictionary entry by a different term changes the output accordingly (70-97% of the new terms appear) with no retraining. We release the models, data and code.