作　　者： (陈宏申）; (刘群）;

机构地区： 中国科学院计算技术研究所智能信息处理重点实验室,中国北京100190 中国科学院大学,中国北京100190

出　　处： 《中国科学：信息科学》 2017年第8期1036-1050,共15页

摘　　要： 统计机器翻译模型,特别是基于句法的翻译模型,其翻译单元在保留足够的翻译信息以及翻译单元在翻译新句子时的泛化能力上始终存在着一个平衡.神经网络被成功用于统计机器翻译模型中的调序和端到端机器翻译中.本文提出了一个新颖的基于神经网络的句法翻译规则编码解码器,依存边转换翻译规则编码解码器(DETED),它利用一条转换翻译规则的源端以及源端的上下文作为输入,以依存边转换翻译规则的目标端作为输出,学习依存边转换翻译规则的源端依存边到目标端依存边的匹配关系.它不仅保留了依存边——这种最简单的句法翻译规则的灵活性,保证了翻译规则的泛化能力,同时通过上下文信息增强了转换翻译规则的匹配能力.编码器–解码器的结构非常简洁,它将翻译规则的源端作为输入,同时解码翻译规则目标端的对应翻译并判别依存边的位置关系.使用编码解码器对解码时所用到的依存边转换翻译规则打分.在3个NIST测试集上的实验显示,相较于基线系统,平均有1.39个BLEU的提升. In existing statistical machine translation models, especially syntax-based models, there has always been a trade-off between the amount of information a translation unit preserves and its ability to generalize when translating new sentences. Neural networks have been successfully employed in reordering and end-to-end machine translation problems. In this paper, we propose a novel syntactic translation rule encoder-decoder based on neural networks. It is a dependency edge transfer rule encoder-decoder（DETED） that leverages the source side of a transfer rule and local context as input, and outputs the target side of that in order to learn the sourceto-target matching of the dependency edge transfer rules. It shares not only the benefit of dependency edge,which is the most relaxed syntactic constraint, in order to ensure its generalization ability, but also the local context as additional information in order to improve its matching ability. The structure of the encoder-decoder is quite concise. With the source side of a translation rule as the input, it decodes the corresponding target side of the translation rule, and makes it clear the positional relation of the dependency edge. The generator is used to re-score the transfer rules when decoding. Experiments on three NIST test sets are presented. The results indicate a significant performance improvement with an average BLEU score of 1.39 above the baseline value.

关 键 词： 依存边 转换 机器翻译 神经网络 编码器 解码器