Bridging Neural Machine Translation and Bilingual Dictionaries
arXiv:1610.07272
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.
arXiv comment10 pages, 2 figures
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- Neural System Combination for Machine Translation
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- Dictionary-based Data Augmentation for Cross-Domain Neural Machine Translation
- Translating Phrases in Neural Machine Translation
- Bilingual Dictionary Based Neural Machine Translation without Using Parallel Sentences
- Incorporating Bilingual Dictionaries for Low Resource Semi-Supervised Neural Machine Translation
- Merging External Bilingual Pairs into Neural Machine Translation
- A Survey on Low-Resource Neural Machine Translation
- Neural Machine Translation: A Review and Survey
- Phrase Table as Recommendation Memory for Neural Machine Translation
- Towards Machine Translation for the Kurdish Language
- Word, Subword or Character? An Empirical Study of Granularity in Chinese-English NMT
- Bilingual Dictionary-based Language Model Pretraining for Neural Machine Translation