We present Latin BERT, a contextual language model for the Latin language,
trained on 642.7 million words from a variety of sources spanning the Classical
era to the 21st century. In a series of case studies, we illustrate the
affordances of this language-specific model both for work in natural language
processing for Latin and in using computational methods for traditional
scholarship: we show that Latin BERT achieves a new state of the art for
part-of-speech tagging on all three Universal Dependency datasets for Latin and
can be used for predicting missing text (including critical emendations); we
create a new dataset for assessing word sense disambiguation for Latin and
demonstrate that Latin BERT outperforms static word embeddings; and we show
that it can be used for semantically-informed search by querying contextual
nearest neighbors. We publicly release trained models to help drive future work
in this space.

我们介绍了 Latin BERT，它是一种针对拉丁语言的上下文语言模型。在多个案例研究中，我们展示了该模型在自然语言处理和传统学术研究中的多种应用，比如在所有三个拉丁语通用语义数据集上都取得了最新的最佳词性标注成果，并通过查询上下文最近邻实现了语义上信息化的搜索。