Numerical Reasoning over Legal Contracts via Relational Database
Numerical reasoning over text requires deep integration between the semantic understanding of the natural language context and the mathematical calculation of the symbolic terms. However, existing approaches are limited in their ability to incorporate domain-specific knowledge and express mathematical formulas over data structures. Delegating logic reasoning to a relational database is a promising approach to enhance the reasoning complexity. We study the problem of distilling natural language text into a relational database with numerical data structure and querying this database to obtain desired answers. Specifically, given a legal contract and a set of date-related questions in natural language, we utilize pre-trained neural network models to create a relational database to retrieve and generate the target dates. We evaluate our method on the CUAD dataset and demonstrate that our approach has high correct answer coverage and reduces a significant amount of incorrect results even without any labels.
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CUAD
