CLAUSEREC: A Clause Recommendation Framework for AI-aided Contract Authoring

• URL: http://arxiv.org/abs/2110.15794v1
• Date: Tue, 26 Oct 2021 09:20:16 GMT
• Title: CLAUSEREC: A Clause Recommendation Framework for AI-aided Contract Authoring
• Authors: Vinay Aggarwal, Aparna Garimella, Balaji Vasan Srinivasan, Anandhavelu N, Rajiv Jain
• Abstract summary: We introduce the task of clause recommendation, as a first step to aid and accelerate the author-ing of contract documents. We propose a two-staged pipeline to first predict if a specific clause type is relevant to be added in a contract, and then recommend the top clauses for the given type based on the contract context.
• Score: 7.3246387015020025
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Contracts are a common type of legal document that frequent in several day-to-day business workflows. However, there has been very limited NLP research in processing such documents, and even lesser in generating them. These contracts are made up of clauses, and the unique nature of these clauses calls for specific methods to understand and generate such documents. In this paper, we introduce the task of clause recommendation, asa first step to aid and accelerate the author-ing of contract documents. We propose a two-staged pipeline to first predict if a specific clause type is relevant to be added in a contract, and then recommend the top clauses for the given type based on the contract context. We pretrain BERT on an existing library of clauses with two additional tasks and use it for our prediction and recommendation. We experiment with classification methods and similarity-based heuristics for clause relevance prediction, and generation-based methods for clause recommendation, and evaluate the results from various methods on several clause types. We provide analyses on the results, and further outline the advantages and limitations of the various methods for this line of research.

Related papers

• Contextual Document Embeddings [77.22328616983417]
  We propose two complementary methods for contextualized document embeddings. First, an alternative contrastive learning objective that explicitly incorporates the document neighbors into the intra-batch contextual loss. Second, a new contextual architecture that explicitly encodes neighbor document information into the encoded representation.
  arXiv  Detail & Related papers  (2024-10-03T14:33:34Z)
• SparseCL: Sparse Contrastive Learning for Contradiction Retrieval [87.02936971689817]
  Contradiction retrieval refers to identifying and extracting documents that explicitly disagree with or refute the content of a query. Existing methods such as similarity search and crossencoder models exhibit significant limitations. We introduce SparseCL that leverages specially trained sentence embeddings designed to preserve subtle, contradictory nuances between sentences.
  arXiv  Detail & Related papers  (2024-06-15T21:57:03Z)
• Investigating Strategies for Clause Recommendation [12.913021807351328]
  We investigate the importance of similar contracts' representation for recommending clauses. We generate clauses for 15 commonly occurring clause types in contracts. We analyze clause recommendations in varying settings using information derived from similar contracts.
  arXiv  Detail & Related papers  (2023-01-21T11:03:47Z)
• Formal Modeling and Analysis of Legal Contracts using ContractCheck [0.0]
  textitContractCheck allows consistency analysis of legal contracts, in particular Sales Purchase Agreements (SPAs) The analysis relies on an encoding of the premises for the execution of the clauses of an SPA as well as the proposed consistency constraints using decidable fragments of first-order logic.
  arXiv  Detail & Related papers  (2022-12-06T22:03:11Z)
• ConReader: Exploring Implicit Relations in Contracts for Contract Clause Extraction [84.0634340572349]
  We study automatic Contract Clause Extraction (CCE) by modeling implicit relations in legal contracts. In this work, we first comprehensively analyze the complexity issues of contracts and distill out three implicit relations commonly found in contracts. We propose a novel framework ConReader to exploit the above three relations for better contract understanding and improving CCE.
  arXiv  Detail & Related papers  (2022-10-17T02:15:18Z)
• Learning a General Clause-to-Clause Relationships for Enhancing Emotion-Cause Pair Extraction [20.850786410336216]
  We propose a novel clause-level encoding model named EA-GAT. E-GAT is designed to aggregate information from different types of clauses. We show that our approach has a significant advantage over all current approaches on the Chinese and English benchmark corpus.
  arXiv  Detail & Related papers  (2022-08-29T12:39:39Z)
• Tradeoffs in Preventing Manipulation in Paper Bidding for Reviewer Assignment [89.38213318211731]
  Despite the benefits of using bids, reliance on paper bidding can allow malicious reviewers to manipulate the paper assignment for unethical purposes. Several different approaches to preventing this manipulation have been proposed and deployed. In this paper, we enumerate certain desirable properties that algorithms for addressing bid manipulation should satisfy.
  arXiv  Detail & Related papers  (2022-07-22T19:58:17Z)
• On the Evaluation of Answer-Agnostic Paragraph-level Multi-Question Generation [57.630606799713526]
  We study the task of predicting a set of salient questions from a given paragraph without any prior knowledge of the precise answer. First, we propose a new method to evaluate a set of predicted questions against the set of references by using the Hungarian algorithm to assign predicted questions to references before scoring the assigned pairs. Second, we compare different strategies to utilize a pre-trained seq2seq model to generate and select a set of questions related to a given paragraph.
  arXiv  Detail & Related papers  (2022-03-09T00:55:54Z)
• Detecting Logical Relation In Contract Clauses [94.85352502638081]
  We develop an approach to automate the extraction of logical relations between clauses in a contract. The resulting approach should help contract authors detecting potential logical conflicts between clauses.
  arXiv  Detail & Related papers  (2021-11-02T19:26:32Z)
• ContractNLI: A Dataset for Document-level Natural Language Inference for Contracts [39.75232199445175]
  We propose "document-level natural language inference (NLI) for contracts" A system is given a set of hypotheses and a contract, and it is asked to classify whether each hypothesis is "entailed by", "contradicting to" or "not mentioned by" (neutral to) the contract. We release the largest corpus to date consisting of 607 annotated contracts.
  arXiv  Detail & Related papers  (2021-10-05T03:22:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.