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Automatic Statutory Reasoning

Primary supervisor

Lizhen Qu

Developing quality AI tools for legal texts is the focus of enormous industry, government and
scholarly attention. The potential benefits include greater efficiency, transparency and access to justice.
Moving beyond the hype requires novel transdisciplinary effort to combine IT and Law expertise.
This project engages this challenge by developing a semi-structured knowledge base (KB) and
reasoners for statutes and cases. The project will also construct corresponding training and evaluation

Student cohort

Single Semester
Double Semester


The goal of the project is to construct a knowledge base based on legal text and develop a novel model for statutory reasoning. You will also assist law students for collecting evaluation dataset.



Holzenberger, Nils, Andrew Blair-Stanek, and Benjamin Van Durme. "A Dataset for Statutory Reasoning in Tax Law Entailment and Question Answering." arXiv preprint arXiv:2005.05257 (2020).

Pertierra, Marcos A., Sarah Lawsky, Erik Hemberg, and Una-May O'Reilly. "Towards Formalizing Statute Law as Default Logic through Automatic Semantic Parsing." In ASAIL@ ICAIL. 2017.

Required knowledge

Students should have excellent grades in machine learning and relevant math courses. Strong programming skills are essential. Preference will be given to students who have previous practical experiences on machine learning tools such as Tensorflow, Pytorch, and ScikitLearn. Students with an interest in pursuing PhD research or careers in research are especially encouraged to apply.