Name
Abstract | ||
|---|---|---|
Despite impressive progress in high-resource settings, Neural Machine Translation (NMT) still struggles in low-resource and out-of-domain scenarios, often failing to match the quality of phrase-based translation. We propose a novel technique that combines back-translation and multilingual NMT to improve performance in these difficult cases. Our technique trains a single model for both directions of a language pair, allowing us to back-translate source or target monolingual data without requiring an auxiliary model. We then continue training on the augmented parallel data, enabling a cycle of improvement for a single model that can incorporate any source, target, or parallel data to improve both translation directions. As a byproduct, these models can reduce training and deployment costs significantly compared to uni-directional models. Extensive experiments show that our technique outperforms standard back-translation in low-resource scenarios, improves quality on cross-domain tasks, and effectively reduces costs across the board. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.18653/v1/w18-2710 | NEURAL MACHINE TRANSLATION AND GENERATION |
DocType | Volume | Citations |
Conference | abs/1805.11213 | 2 |
PageRank | References | Authors |
0.37 | 18 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xing Niu | 1 | 135 | 10.15 |
| Michael J. Denkowski | 2 | 625 | 28.96 |
| Marine Carpuat | 3 | 587 | 51.99 |