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Contextual language models have been trained on Classical languages,including Ancient Greek and Latin, for tasks such as lemmatization,morphological tagging, part of speech tagging, authorship attribution, anddetection of scribal errors. However, high-quality sentence embedding modelsfor these historical languages are significantly more difficult to achieve dueto the lack of training data. In this work, we use a multilingual knowledgedistillation approach to train BERT models to produce sentence embeddings forAncient Greek text. The state-of-the-art sentence embedding approaches forhigh-resource languages use massive datasets, but our distillation approachallows our Ancient Greek models to inherit the properties of these models whileusing a relatively small amount of translated sentence data. We build aparallel sentence dataset using a sentence-embedding alignment method to alignAncient Greek documents with English translations, and use this dataset totrain our models. We evaluate our models on translation search, semanticsimilarity, and semantic retrieval tasks and investigate translation bias. Wemake our training and evaluation datasets freely available athttps://github.com/kevinkrahn/ancient-greek-datasets .

Sentence Embedding Models for Ancient Greek Using Multilingual Knowledge Distillation