Addressing the Rare Word Problem in Neural Machine Translation

Understand

Neural Machine Translation (NMT) is a new approach to machine translation that has shown promising results that are comparable to traditional approaches.

• A significant weakness in conventional NMT systems is their inability to correctly translate very rare words: end-to-end NMTs tend to have relatively small vocabularies with a single unk symbol that represents every possible out-of-vocabulary (OOV) word.
• In this paper, we propose and implement an effective technique to address this problem.
• We train an NMT system on data that is augmented by the output of a word alignment algorithm, allowing the NMT system to emit, for each OOV word in the target sentence, the position of its corresponding word in the source sentence.