Unsupervised Authorship Attribution for Medieval Latin Using Transformer-Based Embeddings

AbstractWe explore the potential of employing transformer-based embeddings in an unsupervised authorship attribution task for medieval Latin. The development of Large Language Models (LLMs) and recent advances in transfer learning alleviate many of the traditional issues associated with authorship attribution in lower-resourced (ancient) languages. Despite this, these methods remain heavily understudied within this domain. Concretely, we generate strong contextual embeddings using a variety of mono -and multilingual transformer models and use these as input for two unsupervised clustering methods: a standard agglomerative clustering algorithm and a self-organizing map. We show that these transformer-based embeddings can be used to generate high-quality and interpretable clusterings, resulting in an attractive alternative to the traditional feature-based methods.