首页> 外文会议>8th Workshop on syntax, semantics and structure in statistical translation 2014 >On the Properties of Neural Machine Translation: Encoder-Decoder Approaches
【24h】

On the Properties of Neural Machine Translation: Encoder-Decoder Approaches

机译:关于神经机器翻译的属性:编码器-解码器方法

获取原文
获取原文并翻译 | 示例

摘要

Neural machine translation is a relatively new approach to statistical machine translation based purely on neural networks. The neural machine translation models often consist of an encoder and a decoder. The encoder extracts a fixed-length representation from a variable-length input sentence, and the decoder generates a correct translation from this representation. In this paper, we focus on analyzing the properties of the neural machine translation using two models; RNN Encoder-Decoder and a newly proposed gated recursive con-volutional neural network. We show that the neural machine translation performs relatively well on short sentences without unknown words, but its performance degrades rapidly as the length of the sentence and the number of unknown words increase. Furthermore, we find that the proposed gated recursive convolutional network learns a grammatical structure of a sentence automatically.
机译:神经机器翻译是一种纯粹基于神经网络的相对较新的统计机器翻译方法。神经机器翻译模型通常由编码器和解码器组成。编码器从可变长度的输入语句中提取固定长度的表示形式,然后解码器从该表示形式生成正确的翻译。在本文中,我们着重于使用两种模型分析神经机器翻译的特性。 RNN编解码器和新提出的门控递归卷积神经网络。我们表明,神经机器翻译在没有未知单词的短句子上表现相对较好,但是随着句子长度和未知单词数量的增加,其性能会迅速下降。此外,我们发现所提出的门控递归卷积网络会自动学习句子的语法结构。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号