Abstract
In the context of globalization, the development of localized translation technology for enterprise online documents is crucial for business promotion. The enterprise online documents are represented by semi-structured text documents with markup tags, while the mainstream neural machine translation methods focus on only the plain text translation. In this research, a Word Alignment based Transformer Model was proposed for markup language translation. Experiments conducted on the Salesforce XML English-Chinese datasets, and the result demonstrated that adding a word alignment model to the translation model can improve the translation model’s performance in translating text with makup tags.
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Acknowledgment
This research was supported by Public Health & Disease Control and Prevention, Major Innovation & Planning Interdisciplinary Platform for the “Double-First Class Initiative, Renmin University of China (No. 2022PDPC), fund for building world-class universities (disciplines) of Renmin University of China. Project No. KYGJA2022001. This research was supported by Public Computing Cloud, Renmin University of China.
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An, J., Tang, Y., Bai, Y., Li, J. (2022). Word Alignment Based Transformer Model for XML Structured Documentation Translation. In: Strauss, C., Cuzzocrea, A., Kotsis, G., Tjoa, A.M., Khalil, I. (eds) Database and Expert Systems Applications. DEXA 2022. Lecture Notes in Computer Science, vol 13426. Springer, Cham. https://doi.org/10.1007/978-3-031-12423-5_24
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DOI: https://doi.org/10.1007/978-3-031-12423-5_24
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