16th International Conference Artificial Intelligence and Law, Conference Proceedings, June 2017, pp. 229-332.
5 Pages Posted: 18 Jun 2017 Last revised: 18 Jul 2017
Date Written: June 12, 2017
This paper constructs a legal text generation and assembly system in the domain of international investment law. We rely on a corpus of 1600 bilateral investment treaties split into 22 600 articles to train a character-level recurrent neural network (char-RNN). Prior work has shown that while char-RNNs can produce legally meaningful texts, its output tends to be repetitive. In this contribution, we remedy this shortcoming by proposing a new framework for RNN-based text production. First, we elicit priors at the training stage to give more weight to under-represented treaty practice. Second, we use q-gram distance and GloVe word embeddings as filters imposed on the generated texts to draw them closer to a target document. Third, we develop a validation routine that compares the distribution of pre-defined legal concepts in actual and generated texts. Our results indicate that the RNN produces texts that are not repetitive and convey meaningful legal concepts. We conclude by showcasing a practical application of our framework by predicting provisions of the USA-China bilateral investment treaty currently under negotiation.
Keywords: recurrent neural network, artificial intelligence, law, document production, investment treaties, international law
Suggested Citation: Suggested Citation
Alschner, Wolfgang and Skougarevskiy, Dmitriy, Towards an Automated Production of Legal Texts Using Recurrent Neural Networks (June 12, 2017). 16th International Conference Artificial Intelligence and Law, Conference Proceedings, June 2017, pp. 229-332.; Ottawa Faculty of Law Working Paper No. 2017-27. Available at SSRN: https://ssrn.com/abstract=2984920