This paper presents machine-learning methods to address various problems in Greek philology. After training a BERT model on the largest premodern Greek dataset used for this purpose to date, we identify and correct previously undetected errors made by scribes in the process of textual transmission, in what is, to our knowledge, the first successful identification of such errors via machine learning. Additionally, we demonstrate the model's capacity to fill gaps caused by material deterioration of premodern manuscripts and compare the model's performance to that of a domain expert. We find that best performance is achieved when the domain expert is provided with model suggestions for inspiration. With such human-computer collaborations in mind, we explore the model's interpretability and find that certain attention heads appear to encode select grammatical features of premodern Greek.

翻译：本文提出了用于解决希腊语文学中多种问题的机器学习方法。在迄今最大规模的近代前希腊语数据集上训练BERT模型后，我们识别并纠正了文本传承过程中抄写员此前未被发现的错误——据我们所知，这是首次通过机器学习成功识别此类错误。此外，我们还展示了该模型填补近代前手稿因材料劣化造成的文本空缺的能力，并将其表现与领域专家进行了比较。研究发现，当领域专家获得模型建议作为灵感来源时，能实现最佳表现。基于这种人机协作的考量，我们探究了模型的可解释性，发现部分注意力头似乎编码了近代前希腊语的特定语法特征。