A Preordered RNN Layer Boosts Neural Machine Translation in Low Resource Settings
This addresses translation quality for low-resource languages like Persian, but is incremental as it builds on existing attention-based methods.
The paper tackled the problem of neural machine translation underperforming in low-resource settings due to data scarcity, and found that augmenting attention-based networks with reordering information improved translation quality by up to 6% BLEU absolute over baselines for English-Persian pairs.
Neural Machine Translation (NMT) models are strong enough to convey semantic and syntactic information from the source language to the target language. However, these models are suffering from the need for a large amount of data to learn the parameters. As a result, for languages with scarce data, these models are at risk of underperforming. We propose to augment attention based neural network with reordering information to alleviate the lack of data. This augmentation improves the translation quality for both English to Persian and Persian to English by up to 6% BLEU absolute over the baseline models.