On Using Monolingual Corpora in Neural Machine Translation
This addresses the challenge of limited parallel data for machine translation, particularly benefiting low-resource language pairs and domain-specific tasks, though it is incremental in nature.
The paper tackled the problem of leveraging monolingual corpora to improve neural machine translation, achieving BLEU score improvements of up to 1.96 on Turkish-English and 0.47 on German-English over baselines.
Recent work on end-to-end neural network-based architectures for machine translation has shown promising results for En-Fr and En-De translation. Arguably, one of the major factors behind this success has been the availability of high quality parallel corpora. In this work, we investigate how to leverage abundant monolingual corpora for neural machine translation. Compared to a phrase-based and hierarchical baseline, we obtain up to $1.96$ BLEU improvement on the low-resource language pair Turkish-English, and $1.59$ BLEU on the focused domain task of Chinese-English chat messages. While our method was initially targeted toward such tasks with less parallel data, we show that it also extends to high resource languages such as Cs-En and De-En where we obtain an improvement of $0.39$ and $0.47$ BLEU scores over the neural machine translation baselines, respectively.