ConReader: Exploring Implicit Relations in Contracts for Contract Clause Extraction

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 2581 - 2594 December 7-11, 2022 ©2022 Association for Computational Linguistics ConReader: Exploring Implicit Relations in Contracts for Contract Clause Extraction∗ Weiwen Xu1, Yang Deng1, Wenqiang Lei2, Wenlong Zhao1, Tat-Seng Chua3, and Wai Lam1 1The Chinese University of Hong Kong 2Sichuan University 3National University of Singapore {wwxu,ydeng,wlam}@se.cuhk.edu.hk {wenqianglei,wenlzhao}@gmail.com, chuats@comp.nus.edu.sg 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.1 1 Introduction Legal Contract Review is a process of thoroughly examining a legal contract before it is signed to en- sure that the content stated in the contract is clear, accurate, complete and free from risks. A key com- ponent to this application is the Contract Clause Ex- traction (CCE), which aims to identify key clauses from the contract for further in-depth review and risk assessment. Typically, CCE consists of two major tasks targeting different query granularities for real-life usages. They are Clause Analysis (CA) and Clause Discovery (CD) 2, where CA aims to ∗ The work described in this paper is substantially sup- ported by a grant from the Research Grant Council of the Hong Kong Special Administrative Region, China (Project Code: 14204418). 1Our code is available at: https://github.com/wwxu21/ ConReader 2CA refers to Contract Analysis in Hendrycks et al. (2021). CD refers to Contract Discovery in Borchmann et al. (2020). identify clauses that belong to a general clause type, while CD aims to identify clauses similar to a spe- cific clause (depicted in Figure 1). CCE is both expensive and time-consuming as it requires legal professionals to manually identify a small num- ber of key clauses from contracts with hundreds of pages in length (Hendrycks et al., 2021). Therefore, there is a pressing need for automating CCE, which assists legal professionals to analyze long and te- dious documents and provides non-professionals with immediate legal guidance. The biggest challenge to automating CCE is the complexities of contracts. In the literature, sim- ply treating contracts as plain text, most pretrained language models perform poorly on CCE (Devlin et al., 2019; Liu et al., 2019). Some works try to simplify CCE from the perspective of contract structure. For example, Chalkidis et al. (2017) as- sign a fixed extraction zone …

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