Coarse-To-Fine Learning for Neural Machine Translation

摘要：

　　In this paper,we address the problem of learning better word representations for neural machine translation(NMT).We propose a novel approach to NMT model training based on coarse-to-fine learning paradigm,which is able to infer better NMT model parameters for a wide range of less-frequent words in the vocabulary.To this end,our proposed method first groups source and target words into a set of hierarchical clusters,then a sequence of NMT models are learned based on it with growing cluster granularity.Each subsequent model inherits model parameters from its previous one and refines them with finergrained word-cluster mapping.Experimental results on public data sets demonstrate that our proposed method significantly outperforms baseline attention-based NMT model on Chinese-English and English-French translation tasks.

关键词： Neural machine translation Coarse-to-fine learning Hierarchical cluster

作者: Zhirui Zhang Shujie Liu Mu Li Ming Zhou Enhong Chen

作者单位: University of Science and Technology of China,Hefei,China Microsoft Research Asia,Beijing,China

会议类型: 国际会议

会议名称: 2018自然语言处理与中文计算国际会议(NLPCC2018)

会议地点: 呼和浩特

会议语种:英文

页码: 316-328

在线出版日期: 2018-08-26（万方平台首次上网日期，不代表论文的发表时间）