Facilitating Terminology Translation with Target Lemma Annotations
This addresses a practical limitation for professional translators by enabling terminology integration without requiring pre-specified inflected forms, though it is incremental as it builds on existing data augmentation techniques.
The paper tackles the problem of integrating bilingual glossaries into machine translation by proposing a source-side data augmentation method that uses target language lemmas, improving translation into morphologically complex languages by up to 7 BLEU points over baselines and 4 BLEU points on average over prior work.
Most of the recent work on terminology integration in machine translation has assumed that terminology translations are given already inflected in forms that are suitable for the target language sentence. In day-to-day work of professional translators, however, it is seldom the case as translators work with bilingual glossaries where terms are given in their dictionary forms; finding the right target language form is part of the translation process. We argue that the requirement for apriori specified target language forms is unrealistic and impedes the practical applicability of previous work. In this work, we propose to train machine translation systems using a source-side data augmentation method that annotates randomly selected source language words with their target language lemmas. We show that systems trained on such augmented data are readily usable for terminology integration in real-life translation scenarios. Our experiments on terminology translation into the morphologically complex Baltic and Uralic languages show an improvement of up to 7 BLEU points over baseline systems with no means for terminology integration and an average improvement of 4 BLEU points over the previous work. Results of the human evaluation indicate a 47.7% absolute improvement over the previous work in term translation accuracy when translating into Latvian.