摘要:神经机器翻译是目前应用最广泛的机器翻译方法，在语料资源丰富的语种上取得了良好的效果.但是在汉语-越南语这类缺乏双语数据的语种上表现不佳.考虑汉语和越南语在语法结构上的差异性，提出一种融合源语言句法解析树的汉越神经机器翻译方法，利用深度优先遍历得到源语言的句法解析树的向量化表示，将句法向量与源语言词嵌入相加作为输入，训练翻译模型.在汉-越语言对上进行了实验，相较于基准系统，获得了0.6个BLUE值的提高.实验结果表明，融合句法解析树可以有效提高在资源稀缺情况下机器翻译模型的性能.



Abstract:Neural machine translation is the most widely used machine translation method at present, and has sound performance in languages with rich corpus resources. However, it does not work well in languages that lack of bilingual data, such as Chinese-Vietnamese. Taking the difference in grammatical structure between different languages into consideration, this study proposes a neural machine translation method that incorporates syntactic parse tree. In this method, a depth-first search is used to obtain the vectorized representation of the syntactic parse tree of the source language, and the translation model is trained by embedding the obtained vectors and the source language embedding as inputs. This method is implemented on Chinese-Vietnamese, language pair and achieves 0.6 BLUE values improvement compared to the baseline system. This experiment shows that the incorporating syntax parse tree can effectively improve the performance of the machine translation model under the resource scarcity.



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