Generalized Data Augmentation for Low-Resource Translation

Understand

Translation to or from low-resource languages LRLs poses challenges for machine translation in terms of both adequacy and fluency.

• Data augmentation utilizing large amounts of monolingual data is regarded as an effective way to alleviate these problems.
• In this paper, we propose a general framework for data augmentation in low-resource machine translation that not only uses target-side monolingual data, but also pivots through a related high-resource language HRL.
• Specifically, we experiment with a two-step pivoting method to convert high-resource data to the LRL, making use of available resources to better approximate the true data distribution of the LRL.