Abstract
We present iterative back-translation, a method for generating increasingly better synthetic parallel data from monolingual data to train neural machine translation systems. Our proposed method is very simple yet effective and highly applicable in practice. We demonstrate improvements in neural machine translation quality in both high and low resourced scenarios, including the best reported BLEU scores for the WMT 2017 German↔English tasks.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 2nd Workshop on Neural Machine Translation and Generation |
| Place of Publication | Melbourne, Australia |
| Publisher | Association for Computational Linguistics |
| Pages | 18-24 |
| Number of pages | 7 |
| Publication status | Published - 20 Jul 2018 |
| Event | 2nd Workshop on Neural Machine Translation and Generation - Melbourne, Australia Duration: 15 Jul 2018 → 20 Jul 2018 https://sites.google.com/site/wnmt18/home https://sites.google.com/site/wnmt18/ |
Conference
| Conference | 2nd Workshop on Neural Machine Translation and Generation |
|---|---|
| Abbreviated title | WNMT 2018 |
| Country/Territory | Australia |
| City | Melbourne |
| Period | 15/07/18 → 20/07/18 |
| Internet address |