ConReader: Exploring Implicit Relations in Contracts for Contract Clause Extraction
Weiwen Xu, Yang Deng, Wenqiang Lei, Wenlong Zhao, Tat-Seng Chua, and, Wai Lam

TL;DR
ConReader is a novel framework that models implicit relations in legal contracts to enhance automatic contract clause extraction, achieving state-of-the-art results and interpretability.
Contribution
This work introduces a comprehensive analysis of contract complexity and proposes ConReader, a framework leveraging three implicit relations for improved clause extraction.
Findings
ConReader outperforms previous methods on two CCE tasks.
It improves interpretability of contract clause predictions.
Achieves state-of-the-art results in both conventional and zero-shot settings.
Abstract
We study automatic Contract Clause Extraction (CCE) by modeling implicit relations in legal contracts. Existing CCE methods mostly treat contracts as plain text, creating a substantial barrier to understanding contracts of high complexity. In this work, we first comprehensively analyze the complexity issues of contracts and distill out three implicit relations commonly found in contracts, namely, 1) Long-range Context Relation that captures the correlations of distant clauses; 2) Term-Definition Relation that captures the relation between important terms with their corresponding definitions; and 3) Similar Clause Relation that captures the similarities between clauses of the same type. Then we propose a novel framework ConReader to exploit the above three relations for better contract understanding and improving CCE. Experimental results show that ConReader makes the prediction more…
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Taxonomy
TopicsArtificial Intelligence in Law · Law, Economics, and Judicial Systems · Law, AI, and Intellectual Property
