Bridging Neural Machine Translation and Bilingual Dictionaries
This addresses a specific bottleneck in machine translation for handling rare words, but it is incremental as it builds on existing NMT frameworks.
The paper tackles the problem of integrating neural machine translation with bilingual dictionaries for rare words, proposing methods to transform dictionaries into sentence pairs, and shows that these methods improve translation quality and enable correct translations for most rare words covered by the dictionary.
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.