Bi-Directional Neural Machine Translation with Synthetic Parallel Data

Understand

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.