Ancient Chinese is the essence of Chinese culture. There are several natural language processing tasks of ancient Chinese domain, such as ancient-modern Chinese translation, poem generation, and couplet generation. Previous studies usually use the supervised models which deeply rely on parallel data. However, it is difficult to obtain large-scale parallel data of ancient Chinese. In order to make full use of the more easily available monolingual ancient Chinese corpora, we release AnchiBERT, a pre-trained language model based on the architecture of BERT, which is trained on large-scale ancient Chinese corpora. We evaluate AnchiBERT on both language understanding and generation tasks, including poem classification, ancient-modern Chinese translation, poem generation, and couplet generation. The experimental results show that AnchiBERT outperforms BERT as well as the non-pretrained models and achieves state-of-the-art results in all cases.
@article{arxiv.2009.11473,
title = {AnchiBERT: A Pre-Trained Model for Ancient ChineseLanguage Understanding and Generation},
author = {Huishuang Tian and Kexin Yang and Dayiheng Liu and Jiancheng Lv},
journal= {arXiv preprint arXiv:2009.11473},
year = {2021}
}