中圖分類號(hào)：TP391 文獻(xiàn)標(biāo)志碼：A DOI: 10.16157/j.issn.0258-7998.233887
中文引用格式： 曾文顥，張勇丙，余正濤，等. 融入翻譯記憶庫(kù)的法律領(lǐng)域神經(jīng)機(jī)器翻譯方法[J]. 電子技術(shù)應(yīng)用，2023，49(9)：39-45.
英文引用格式： Zeng Wenhao，Zhang Yongbing，Yu Zhengtao，et al. Legal neural machine translation based on translation memory[J]. Application of Electronic Technique，2023，49(9)：39-45.

Legal neural machine translation based on translation memory

Abstract： Neural machine translation for the legal domain is of great value for application scenarios such as contract text translation. Due to the scarcity of bilingual corpora in the legal domain, the machine translation performance is still not satisfactory. A practical method to address this problem is to integrate prior knowledge such as translation memory(TM) or templates. However, texts in the legal domain mostly have fixed expression structures and precise wording specifications. The performance of translation in the legal field can be further improved by using both sentence structure information and semantic information in the translation memory. Based on this, this paper proposes a new framework that uses monolingual TM and performs learnable memory retrieval in a cross-language manner. Firstly, this monolingual translation memories contain translation memory and translation template, which can provide richer external knowledge to the model. Secondly, the retrieval model and the translation model can be jointly optimized. Experiments on the MHLAW dataset show that this model surpasses baseline models up to 1.28 BLEU points.

Key words : neural machine translation；semantic information；structure information；translation memory；translation template