Ancient–Modern Chinese Translation with a New Large Training Dataset

Author:

Liu Dayiheng1ORCID,Yang Kexin2,Qu Qian2,Lv Jiancheng1

Affiliation:

1. College of Computer Science, State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Chengdu, China

2. College of Computer Science, Sichuan University, Chengdu, China

Abstract

Ancient Chinese brings the wisdom and spirit culture of the Chinese nation. Automatic translation from ancient Chinese to modern Chinese helps to inherit and carry forward the quintessence of the ancients. However, the lack of large-scale parallel corpus limits the study of machine translation in ancient–modern Chinese. In this article, we propose an ancient–modern Chinese clause alignment approach based on the characteristics of these two languages. This method combines both lexical-based information and statistical-based information, which achieves 94.2 F1-score on our manual annotation Test set. We use this method to create a new large-scale ancient–modern Chinese parallel corpus that contains 1.24M bilingual pairs. To our best knowledge, this is the first large high-quality ancient–modern Chinese dataset. Furthermore, we analyzed and compared the performance of the SMT and various NMT models on this dataset and provided a strong baseline for this task.

Funder

State Key Program of National Science Foundation of China

National Natural Science Fund for Distinguished Young Scholar

Publisher

Association for Computing Machinery (ACM)

Subject

General Computer Science

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