Court decisions are traditionally long and complex documents. To make things worse, it is not uncommon for a lawyer to only be interested in the operative part of the judgment (for example, the outcome of the trial). In fact, in general, it is pretty standard to be looking for a specific legal aspect, which can quickly feel like looking for a needle in a haystack. As such, our goal was to detect the underlying structure of decisions on Doctrine (i.e. the table of contents) to help users navigate them more easily.
Decisions can be seen as small stories. While humans can understand them because they are naturally context-aware and have some expectations, how should an algorithm operate? In order to address this challenging issue, we trained a neural network (bi-LSTM with attention) using PyTorch to help us predict a suitable table of contents given a free text decision. This talk gets into more details about our methodology and results
Thursday 17 October 2019 at 9:30am PDT
- Doctrine blog post: https://blog.doctrine.fr/structuring-legal-documents-with-deep-learning/
- Paris NLP, a meetup organized by Doctrine.fr every other month in Paris to discuss research and applied NLP projects: https://www.meetup.com/Paris-NLP/