Learning to Check Contract Inconsistencies

TL;DR

This paper introduces a novel end-to-end framework called Pair-wise Blank Resolution (PBR) for automatically detecting inconsistencies in contract forms, significantly reducing manual review efforts.

Contribution

The paper formulates the Contract Inconsistency Checking (CIC) problem and proposes the BlankCoder, a two-stage attention model, to effectively model meaningless blanks in contracts.

Findings

Achieves 94.05% balanced accuracy on real-world datasets.

Attains 90.90% F1 score in contract inconsistency detection.

Demonstrates high effectiveness of the PBR framework.

Abstract

Contract consistency is important in ensuring the legal validity of the contract. In many scenarios, a contract is written by filling the blanks in a precompiled form. Due to carelessness, two blanks that should be filled with the same (or different)content may be incorrectly filled with different (or same) content. This will result in the issue of contract inconsistencies, which may severely impair the legal validity of the contract. Traditional methods to address this issue mainly rely on manual contract review, which is labor-intensive and costly. In this work, we formulate a novel Contract Inconsistency Checking (CIC) problem, and design an end-to-end framework, called Pair-wise Blank Resolution (PBR), to solve the CIC problem with high accuracy. Our PBR model contains a novel BlankCoder to address the challenge of modeling meaningless blanks. BlankCoder adopts a two-stage attention mechanism that adequately associates a meaningless blank with its relevant descriptions while avoiding the incorporation of irrelevant context words. Experiments conducted on real-world datasets show the promising performance of our method with a balanced accuracy of 94.05% and an F1 score of 90.90% in the CIC problem.