ClauseRec: A Clause Recommendation Framework for AI-aided Contract Authoring (2021.emnlp-main)
Copied to clipboard
| Challenge: | Contracts are a common type of legal document that frequent in business workflows, but there has been limited NLP research in understanding and generating them. |
| Approach: | They propose a task of clause recommendation to help automate contract authoring . they first predict if a specific clause type is relevant to be added in a contract . then they propose two-staged pipeline to recommend top clauses based on the contract context . |
| Outcome: | The proposed pipeline predicts if a clause type is relevant to be added in a contract and recommends the top clauses for the given type based on the contract context. |
Similar Papers
Annotation and Classification of Relevant Clauses in Terms-and-Conditions Contracts (2024.lrec-main)
Copied to clipboard
| Challenge: | Using Large Language Models (LLMs) as foundational models, we propose a new annotation scheme to classify different types of clauses in Terms-and-Conditions contracts. |
| Approach: | They propose to use a new annotation scheme to classify clauses in Terms-and-Conditions contracts to support legal experts in identifying and assessing problematic issues. |
| Outcome: | The proposed annotation scheme achieves accuracies ranging from .79 to .95 on validation tasks. |
Building Context-aware Clause Representations for Situation Entity Type Classification (D18-1)
Copied to clipboard
| Challenge: | Existing models for categorizing clauses based on situation entity types do not provide accurate results. |
| Approach: | They propose to build context-aware clause representations for predicting situation entity types of clauses by modeling context influences and inter-dependencies of clause. |
| Outcome: | The proposed model achieves state-of-the-art performance on genre-rich dataset MASC+Wiki . |
ContractNLI: A Dataset for Document-level Natural Language Inference for Contracts (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Contract review is a time-consuming procedure that costs companies millions of dollars each year . linguistic characteristics of contracts, such as negations by exceptions, contribute to the difficulty of this task . |
| Approach: | They propose a document-level natural language inference (NLI) task for contracts . they annotate and release the largest corpus to date consisting of 607 annotated contracts a linguistically rich system is proposed . |
| Outcome: | The proposed system is based on a contract review task that includes 607 annotated contracts. |
Contract Discovery: Dataset and a Few-Shot Semantic Retrieval Challenge with Competitive Baselines (2020.findings-emnlp)
Copied to clipboard
Łukasz Borchmann, Dawid Wisniewski, Andrzej Gretkowski, Izabela Kosmala, Dawid Jurkiewicz, Łukasz Szałkiewicz, Gabriela Pałka, Karol Kaczmarek, Agnieszka Kaliska, Filip Graliński
| Challenge: | Existing methods for detecting text fragments are not suitable for contract discovery, since it requires manual definition of a few examples, followed by conventional information. |
| Approach: | They propose a task where legal clauses are extracted from documents, given a few examples of similar clauses from other legal acts. |
| Outcome: | The proposed task differs substantially from conventional NLI and shared tasks on legal information extraction. |
Generating Clarification Questions for Disambiguating Contracts (2024.lrec-main)
Copied to clipboard
| Challenge: | Contractual clauses are obligatory and can detail downstream implementation activities . however, contract ambiguities can be difficult to comprehend and can lead to errors . |
| Approach: | They propose a legal NLP task that generates clarification questions for contracts . they propose generating questions that identify contract ambiguities on a document level . |
| Outcome: | The proposed task generates clarification questions for contracts that detect ambiguities on a document level and can generate an F2 score of 0.87. |
ACORD: An Expert-Annotated Retrieval Dataset for Legal Contract Drafting (2025.acl-long)
Copied to clipboard
Steven H Wang, Maksim Zubkov, Kexin Fan, Sarah Harrell, Yuyang Sun, Wei Chen, Andreas Plesner, Roger Wattenhofer
| Challenge: | Contract clause retrieval is critical to contract drafting because of its high quality and complexity. |
| Approach: | They propose the first expert-annotated benchmark specifically designed for contract clause retrieval . ACORD focuses on complex contract clauses such as Limitation of Liability, Indemnification, Change of Control . |
| Outcome: | The atticus clause retrieval dataset shows promising results but needs improvement . the benchmark can be used as an IR benchmark for the NLP community . |
Effective Inter-Clause Modeling for End-to-End Emotion-Cause Pair Extraction (2020.acl-main)
Copied to clipboard
| Challenge: | Emotion-cause pair extraction aims to extract all emotion clauses coupled with their cause clauses from a given document. |
| Approach: | They propose a one-step neural approach which emphasizes inter-clause modeling to perform end-to-end extraction. |
| Outcome: | The proposed method outperforms existing methods in the extraction of emotion-cause pairs . it emphasizes inter-clause modeling to perform end-to-end extraction . |
ConReader: Exploring Implicit Relations in Contracts for Contract Clause Extraction (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing CCE methods treat contracts as plain text, creating a barrier to understanding complex contracts. |
| Approach: | They propose a framework to model implicit relations in legal contracts to improve contract understanding . they propose Term-Definition Relation captures the relation between important terms and their definitions . |
| Outcome: | The proposed framework improves on two CCE tasks in conventional and zero-shot settings. |
Machine-Aided Annotation for Fine-Grained Proposition Types in Argumentation (2020.lrec-1)
Copied to clipboard
| Challenge: | a corpus of 2016 debates and commentary contains 4,648 argumentative propositions annotated with fine-grained proposition types. |
| Approach: | They propose a machine learning-human workflow for annotating for four complex proposition types . they demonstrate with preliminary analysis of rhetorical strategies and structure in presidential debates . |
| Outcome: | The proposed method can be used by technical researchers seeking more nuanced representations of argument . it can also be used to analyze rhetorical strategies and structure in presidential debates . |
Automatic Nominalization of Clauses through Textual Entailment (2022.coling-1)
Copied to clipboard
| Challenge: | Past research on clause nominalization has focused on replacement of the head verb with a deverbal noun and resource development to support the task. |
| Approach: | They propose to use a textual entailment model to optimize the position and POS of nominal arguments by fine-tuning a model on the task. |
| Outcome: | The proposed model outperforms unsupervised approaches on the nominalization task and outperformed a state-of-the-art neural language model. |