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 the potential benefits of (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 sub- missions ranked second for Kazakh-English and third for English-Kazakh in terms of the BLEU automatic evaluation metric.
|Titel||Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)|
|Plaats van productie||Forence, Italy|
|Uitgeverij||Association for Computational Linguistics (ACL)|
|Status||Published - 1-aug-2019|