融合术语信息的新能源专利机器翻译研究

Research on New Energy Patent Machine Translation Integrating Terminology Information

传统的神经机器翻译模型是一个黑盒子,并不能有效把术语信息添加进去。而利用用户提供的术语词典来联合训练神经机器翻译模型具有实际意义。据此,该文提出融入术语信息的新能源领域Transformer专利机器翻译模型,使用将源端术语替换为目标端术语以及在源端术语后增添目标端术语两种手段进行术语信息融合,实验表明,在构建的新能源领域专利汉英平行语料库和术语库上,提出的专利翻译模型优于Transformer基准模型。并评测了其在人工构建的数据集、中国专利信息中心的数据集及世界知识产权局的数据集上的翻译效果。

The traditional neural machine translation is a black box and cannot effectively add terminology information.It is of practical significance to use term provided by the user to jointly train the neural machine translation model.Accordingly,we propose a new energy transformer patent machine translation model with terminology information incorporated.The source term is replaced with the target term and the target term is added after the source term to fusing the terminology information.Experimentsal results on the Chinese-English task with patent termbase in the field of new energy show that the proposed patent translation model is better than the Transformer baseline model,as well as the translation quality analysis on three datasets.