Addressing Troublesome Words in Neural Machine Translation
Yang Zhao, Jiajun Zhang, Zhongjun He, Chengqing Zong, Hua Ren Wu · 2018
One of the weaknesses of Neural Machine Translation (NMT) is in handling lowfrequency and ambiguous words, which we refer as troublesome words.To address this problem, we propose a novel memoryenhanced 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 Chineseto-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.