  This paper demonstrate how NLP can be used to address an unmet need of the
legal community and increase access to justice. The paper introduces Legal
Precedent Prediction (LPP), the task of predicting relevant passages from
precedential court decisions given the context of a legal argument. To this
end, the paper showcases a BERT model, trained on 530,000 examples of legal
arguments made by U.S. federal judges, to predict relevant passages from
precedential court decisions given the context of a legal argument. In 96% of
unseen test examples the correct target passage is among the top-10 predicted
passages. The same model is able to predict relevant precedent given a short
summary of a complex and unseen legal brief, predicting the precedent that was
actually cited by the brief's co-author, former U.S. Solicitor General and
current U.S. Supreme Court Justice Elena Kagan.
