Sentence Embedding Models for Ancient Greek Using Multilingual Knowledge Distillation
This work addresses the problem of limited resources for historical language processing, specifically for Ancient Greek, by enabling sentence embedding models through distillation, though it is incremental as it adapts existing methods to a new domain.
The authors tackled the lack of training data for high-quality sentence embeddings in Ancient Greek by using multilingual knowledge distillation to train BERT models, achieving competitive performance on translation search, semantic similarity, and retrieval tasks with a relatively small translated dataset.
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, and detection of scribal errors. However, high-quality sentence embedding models for these historical languages are significantly more difficult to achieve due to the lack of training data. In this work, we use a multilingual knowledge distillation approach to train BERT models to produce sentence embeddings for Ancient Greek text. The state-of-the-art sentence embedding approaches for high-resource languages use massive datasets, but our distillation approach allows our Ancient Greek models to inherit the properties of these models while using a relatively small amount of translated sentence data. We build a parallel sentence dataset using a sentence-embedding alignment method to align Ancient Greek documents with English translations, and use this dataset to train our models. We evaluate our models on translation search, semantic similarity, and semantic retrieval tasks and investigate translation bias. We make our training and evaluation datasets freely available at https://github.com/kevinkrahn/ancient-greek-datasets .