Ancient-Modern Chinese Translation with a Large Training Dataset

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 paper, 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 which 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.

arXiv commentTo appear in the ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP)

References in corpus (6)

• Adam: A Method for Stochastic Optimization
• Sequence to Sequence Learning with Neural Networks
• Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
• Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
• Towards Robust Neural Machine Translation
• A Generalized Constraint Approach to Bilingual Dictionary Induction for Low-Resource Language Families

Cited by in corpus (4)

• CCPM: A Chinese Classical Poetry Matching Dataset
• Restoring and Mining the Records of the Joseon Dynasty via Neural Language Modeling and Machine Translation
• AnchiBERT: A Pre-Trained Model for Ancient ChineseLanguage Understanding and Generation
• Time-Aware Ancient Chinese Text Translation and Inference