ConReader: Exploring Implicit Relations in Contracts for Contract Clause Extraction
This addresses the problem of understanding complex legal contracts for automated legal analysis, though it appears incremental as it builds on existing CCE methods by incorporating specific relations.
The paper tackled automatic Contract Clause Extraction (CCE) by modeling implicit relations in legal contracts, achieving new state-of-the-art results on two CCE tasks in both conventional and zero-shot settings.
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 interpretable and achieves new state-of-the-art on two CCE tasks in both conventional and zero-shot settings.