ConReader: Exploring Implicit Relations in Contracts for Contract Clause Extraction

Weiwen Xu, Yang Deng, Wenqiang Lei, Wenlong Zhao, Tat-Seng Chua, Wai Lam

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 interpretable and achieves new state-of-the-art on two CCE tasks in both conventional and zero-shot settings.
Anthology ID:
2022.emnlp-main.166
Volume:
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Venue:
EMNLP
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Publisher:
Association for Computational Linguistics
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Pages:
2581–2594
Language:
URL:
https://aclanthology.org/2022.emnlp-main.166
DOI:
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Cite (ACL):
Weiwen Xu, Yang Deng, Wenqiang Lei, Wenlong Zhao, Tat-Seng Chua, and Wai Lam. 2022. ConReader: Exploring Implicit Relations in Contracts for Contract Clause Extraction. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 2581–2594, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
ConReader: Exploring Implicit Relations in Contracts for Contract Clause Extraction (Xu et al., EMNLP 2022)
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