Neural Machine Translation for English–Kazakh with Morphological Segmentation and Synthetic DataToral Ruiz, A., Edman, L., Spenader, J. & Yeshmagambetova, G., 1-Aug-2019, Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1). Forence, Italy: Association for Computational Linguistics (ACL), Vol. 2. p. 386-392 7 p.
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review
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.
|Title of host publication||Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)|
|Place of Publication||Forence, Italy|
|Publisher||Association for Computational Linguistics (ACL)|
|Number of pages||7|
|Publication status||Published - 1-Aug-2019|
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