Bridging Neural Machine Translation and Bilingual Dictionaries

Abstract

Neural Machine Translation (NMT) has become the new state-of-the-art in several language pairs. However, it remains a challenging problem how to integrate NMT with a bilingual dictionary which mainly contains words rarely or never seen in the bilingual training data. In this paper, we propose two methods to bridge NMT and the bilingual dictionaries. The core idea behind is to design novel models that transform the bilingual dictionaries into adequate sentence pairs, so that NMT can distil latent bilingual mappings from the ample and repetitive phenomena. One method leverages a mixed word/character model and the other attempts at synthesizing parallel sentences guaranteeing massive occurrence of the translation lexicon. Extensive experiments demonstrate that the proposed methods can remarkably improve the translation quality, and most of the rare words in the test sentences can obtain correct translations if they are covered by the dictionary.

10 pages, 2 figures

References in corpus (4)

• Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
• On Using Monolingual Corpora in Neural Machine Translation
• Is Neural Machine Translation Ready for Deployment? A Case Study on 30 Translation Directions
• Neural Machine Translation with Supervised Attention

Cited by in corpus (4)

• Translating Phrases in Neural Machine Translation
• Bilingual Dictionary Based Neural Machine Translation without Using Parallel Sentences
• Merging External Bilingual Pairs into Neural Machine Translation
• Word, Subword or Character? An Empirical Study of Granularity in Chinese-English NMT