Neural Machine Translation for English–Kazakh with Morphological Segmentation and Synthetic Data
Antonio Toral, Lukas Edman, Galiya Yeshmagambetova, Jennifer K. Spenader · 2019
This paper presents the systems submitted by the University of Groningen to the English-Kazakh language pair (both translation directions) for the WMT 2019 news translation task.We explore potential benefits from using (i) morphological segmentation (both unsupervised and rule-based), given the agglutinative nature of Kazakh, (ii) data from two additional languages (Turkish and Russian), given the scarcity of English-Kazakh data, and (iii) synthetic data, both for the source and for the target language.Our best submissions ranked second for Kazakh→English and third for English→Kazakh in terms of the BLEU automatic evaluation metric.