  Lawyers and judges spend a large amount of time researching the proper legal
authority to cite while drafting decisions. In this paper, we develop a
citation recommendation tool that can help improve efficiency in the process of
opinion drafting. We train four types of machine learning models, including a
citation-list based method (collaborative filtering) and three context-based
methods (text similarity, BiLSTM and RoBERTa classifiers). Our experiments show
that leveraging local textual context improves recommendation, and that deep
neural models achieve decent performance. We show that non-deep text-based
methods benefit from access to structured case metadata, but deep models only
benefit from such access when predicting from context of insufficient length.
We also find that, even after extensive training, RoBERTa does not outperform a
recurrent neural model, despite its benefits of pretraining. Our behavior
analysis of the RoBERTa model further shows that predictive performance is
stable across time and citation classes.
