Neural Machine Translation for English–Kazakh with Morphological Segmentation and Synthetic Data

Antonio Toral, Lukas Edman, Galiya Yeshmagambetova, Jennifer Spenader


Abstract
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 submissions ranked second for Kazakh→English and third for English→Kazakh in terms of the BLEU automatic evaluation metric.
Anthology ID:
W19-5343
Volume:
Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)
Month:
August
Year:
2019
Address:
Florence, Italy
Venue:
WMT
SIG:
SIGMT
Publisher:
Association for Computational Linguistics
Note:
Pages:
386–392
Language:
URL:
https://aclanthology.org/W19-5343
DOI:
10.18653/v1/W19-5343
Bibkey:
Cite (ACL):
Antonio Toral, Lukas Edman, Galiya Yeshmagambetova, and Jennifer Spenader. 2019. Neural Machine Translation for English–Kazakh with Morphological Segmentation and Synthetic Data. In Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1), pages 386–392, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Neural Machine Translation for English–Kazakh with Morphological Segmentation and Synthetic Data (Toral et al., WMT 2019)
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PDF:
https://preview.aclanthology.org/author-url/W19-5343.pdf