A Character-Aware Encoder for Neural Machine Translation

	A Character-Aware Encoder for Neural Machine Translation
	Yang Z(杨振); Chen W(陈炜); Wang F(王峰); Chen W(陈伟)
	2016
会议名称	International Conference on Computational Linguistics
会议日期	2016-12-11
会议地点	日本大阪
摘要	This article proposes a novel character-aware neural machine translation (NMT) model that views the input sequences as sequences of characters rather than words. On the use of row convolution (Amodei et al., 2015), the encoder of the proposed model composes word-level information from the input sequences of characters automatically. Since our model doesn’t rely on the boundaries between each word (as the whitespace boundaries in English), it is also applied to languages without explicit word segmentations (like Chinese). Experimental results on Chinese-English translation tasks show that the proposed character-aware NMT model can achieve comparable translation performance with the traditional word based NMT models. Despite the target side is still word based, the proposed model is able to generate much less unknown words.
文献类型	会议论文
条目标识符	http://ir.ia.ac.cn/handle/173211/41128
专题	复杂系统认知与决策实验室_听觉模型与认知计算数字内容技术与服务研究中心
通讯作者	Chen W(陈伟)
推荐引用方式 GB/T 7714	Yang Z,Chen W,Wang F,et al. A Character-Aware Encoder for Neural Machine Translation[C],2016.