Tag-less Back-Translation
This addresses the problem of inefficient data utilization in low-resource neural machine translation, offering an incremental improvement over existing back-translation methods.
The paper tackles the inefficiency of standard back-translation in neural machine translation by treating synthetic and authentic data as out-of-domain and in-domain, respectively, using pre-training and fine-tuning, and shows that this tag-less approach outperforms standard and tagged methods on low-resource English-Vietnamese and English-German tasks.
An effective method to generate a large number of parallel sentences for training improved neural machine translation (NMT) systems is the use of the back-translations of the target-side monolingual data. The standard back-translation method has been shown to be unable to efficiently utilize the available huge amount of existing monolingual data because of the inability of translation models to differentiate between the authentic and synthetic parallel data during training. Tagging, or using gates, has been used to enable translation models to distinguish between synthetic and authentic data, improving standard back-translation and also enabling the use of iterative back-translation on language pairs that underperformed using standard back-translation. In this work, we approach back-translation as a domain adaptation problem, eliminating the need for explicit tagging. In the approach -- \emph{tag-less back-translation} -- the synthetic and authentic parallel data are treated as out-of-domain and in-domain data respectively and, through pre-training and fine-tuning, the translation model is shown to be able to learn more efficiently from them during training. Experimental results have shown that the approach outperforms the standard and tagged back-translation approaches on low resource English-Vietnamese and English-German neural machine translation.