CASIA OpenIR  > 数字内容技术与服务研究中心  > 听觉模型与认知计算
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
Conference NameInternational Conference on Computational Linguistics
Conference Date2016-12-11
Conference Place日本大阪
AbstractThis 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.
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/19651
Collection数字内容技术与服务研究中心_听觉模型与认知计算
Corresponding AuthorChen W(陈伟)
Affiliation中国科学院自动化研究所
First Author Affilication中国科学院自动化研究所
Recommended Citation
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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