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/173211/19651
专题数字内容技术与服务研究中心_听觉模型与认知计算
通讯作者Chen W(陈伟)
作者单位中国科学院自动化研究所
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.
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