  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.
