Towards Meaningful Paragraph Embeddings for Data-Scarce Domains: A Case Study in the Legal Domain

Abstract

Creating meaningful text embeddings using BERT-based language models involves pre-training on large amounts of data. For domain-specific use cases where data is scarce (e.g., the law enforcement domain) it might not be feasible to pre-train a whole new language model. In this paper, we examine how extending BERT-based tokenizers and further pre-training BERT-based models can benefit downstream classification tasks. As a proxy for domain-specific data, we use the European Convention of Human Rights (ECtHR) dataset. We find that for down-stream tasks, further pre-training a language model on a small domain dataset can rival models that are completely retrained on large domain datasets. This indicates that completely retraining a language model may not be necessary to improve down-stream task performance. Instead, small adaptions to existing state-of-the-art language models like BERT may suffice.