Addressing troublesome words in neural machine translation

Abstract

One of the weaknesses of Neural Machine Translation (NMT) is in handling low-frequency and ambiguous words, which we refer as troublesome words. To address this problem, we propose a novel memory-enhanced NMT method. First, we investigate different strategies to define and detect the troublesome words. Then, a contextual memory is constructed to memorize which target words should be produced in what situations. Finally, we design a hybrid model to dynamically access the contextual memory so as to correctly translate the troublesome words. The extensive experiments on Chinese-to-English and English-to-German translation tasks demonstrate that our method significantly outperforms the strong baseline models in translation quality, especially in handling troublesome words.