Cross-Domain Contract Element Extraction with a Bi-directional Feedback Clause-Element Relation Network

Abstract

Contract element extraction (CEE) is the novel task of automatically identifying and extracting legally relevant elements such as contract dates, payments, and legislation references from contracts. Automatic methods for this task view it as a sequence labeling problem and dramatically reduce human labor. However, as contract genres and element types may vary widely, a significant challenge for this sequence labeling task is how to transfer knowledge from one domain to another, i.e., cross-domain CEE. Cross-domain CEE differs from cross-domain named entity recognition (NER) in two important ways. First, contract elements are far more fine-grained than named entities, which hinders the transfer of extractors. Second, the extraction zones for cross-domain contract element extraction (CEE) are much larger than for cross-domain NER. As a result, the contexts of elements from different domains can be more diverse.We propose a framework, the Bi-directional Feedback cLause-Element relaTion network (Bi-FLEET), for the cross-domain CEE task that addresses the above challenges. Bi-FLEET has three main components: (1) a context encoder, (2) a clause-element relation encoder, and (3) an inference layer. To incorporate invariant knowledge about element and clause types, a clause-element graph is constructed across domains and a hierarchical graph neural network is adopted in the clause-element relation encoder. To reduce the influence of context variations, a multi-task framework with a bidirectional feedback scheme is designed in the inference layer, conducting both clause classification and element extraction. The experimental results over both cross-domain NER and CEE tasks show that Bi-FLEET significantly outperforms state-of-the-art baselines.

ConReader: Exploring Implicit Relations in Contracts for Contract Clause Extraction

“…They release corpora for automatic contract review, allowing neural models to get surprising performance (Chalkidis and Androutsopoulos, 2017;Chalkidis et al, 2019). Recently, studies grow increasing attention on CCE to extract clauses, which are complete units in contracts, and carefully select a large number of clause types worth human attention (Borchmann et al, 2020;Wang et al, 2021b;Hendrycks et al, 2021). Due to the repetition of contract language that new contracts usually follow the template of old contracts (Simonson et al, 2019), existing methods tend to incorporate structure information to tackle CCE.…”

Generalizing Few-Shot Named Entity Recognizers to Unseen Domains with Type-Related Features

“…In summary, our contributions are: (i) to the best of our knowledge, ours is the first work to study prompt learning for few-shot cross-domain NER; (ii) we develop a mutual information-based approach to identify important entity type-related features from the source domain; (iii) we design a two-stage scheme that generates and incorporates a prompt that is highly relevant to the source domain for each new example, effectively mitigating the gap between source and unseen domains; and (iv) experimental results show that our proposed PLTR achieves state-of-the-art performance on both in-domain and cross-domain datasets. et al, 2013;Lee et al, 2018;Yang et al, 2017;Jia et al, 2019;Jia and Zhang, 2020;Zheng et al, 2022;Hu et al, 2023;Wang et al, 2021) Ma et al, 2022;Lee et al, 2022;Das et al, 2022;Chen et al, 2022b;Dong et al, 2023;Fang et al, 2023). In particular, Das et al (2022) incorporate contrastive learning techniques with prompts to better capture label dependencies.…”

Text Mining Legal Documents for Clause Extraction

“…Early software solutions to extract the legal clauses from the documents have been mainly rules-based, requiring specialised teams to review large volumes of documents to look for variations in each clause type and write complex rules to extract these terms from other documents [2]. As machine learning and artificial intelligence have developed over the years, new software solutions have been developed for the legal industry.…”