Word Rewarding Model Using a Bilingual Dictionary for Neural Machine Translations

Abstract

Even though outputs of neural machine translation are more fluent compared to those of conventional phrase-based statistical machine translation, under and over generation are still major problems. While the translation quality of phrase-based statistical machine translation has improved due to the use of a bilingual dictionary by the decoder constraint, the same approach cannot be directly applied to neural machine translation. This paper proposes a rewarding model to apply the bilingual dictionary to neural machine translation. The proposed model first predicts the target words for the translation using the bilingual dictionary and then increases their decoder output probabilities at an inference. As the model uses the bilingual dictionary as an independent resource for the neural model, it can easily update or change the dictionary if required. The proposed model was found to improve translation quality even though it has less computational complexity than lexically constrained decoding that forces output of specified words. The results also confirmed that when combined with a method that biased the decoder to output dictionary entries using attention weights, the proposed method further improved the translation quality.