论文标题
Anchibert：一种用于古老的Chin语言理解和产生的预训练模型
AnchiBERT: A Pre-Trained Model for Ancient ChineseLanguage Understanding and Generation
论文作者
论文摘要
古代中国人是中国文化的本质。古代中国领域有几种自然语言处理任务，例如古代现代的中国翻译，诗歌产生和对联。以前的研究通常使用深层依赖并联数据的监督模型。但是，很难获得古代中国人的大规模平行数据。为了充分利用更容易获得的单语言中国古代语料库，我们发布了基于伯特（Bert）建筑的预先培训的语言模型，该模型接受了大规模古代中国语料库的培训。我们评估了Anchibert的语言理解和发电任务，包括诗歌分类，古代现代的中文翻译，诗歌产生和对联的产生。实验结果表明，在所有情况下，Anchibert的表现都优于BERT以及未经预告的模型，并实现了最先进的结果。
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.
