Name
Title | ||
|---|---|---|
Neural Machine Translation for English-Kazakh with Morphological Segmentation and Synthetic Data |
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 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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.18653/v1/w19-5343 | FOURTH CONFERENCE ON MACHINE TRANSLATION (WMT 2019) |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Antonio Toral | 1 | 52 | 10.60 |
| Lukas Edman | 2 | 0 | 0.34 |
| Galiya Yeshmagambetova | 3 | 0 | 0.34 |
| Jennifer Spenader | 4 | 0 | 0.34 |