Norm Conflict Identification Using a Convolutional Neural Network

João Paulo Aires, Felipe Meneguzzi

Research output: Chapter in Book/Report/Conference proceedingChapter

3 Citations (Scopus)


Contracts formally represent agreements between two or more parties as deontic statements or norms within their clauses. Norms may conflict between each other if not carefully designed, which may invalidate entire contracts. Human reviewers invest great effort to write conflict-free contracts that, for complex and long contracts, can be time-consuming and error-prone. In this work, we develop an approach to automate the identification of potential conflicts between norms in contracts. We build a two-phase approach that uses traditional machine learning together with a convolutional neural network to extract and compare norms in order to identify conflicts between them. Using a manually annotated and artificially generated set of conflicts as train and test set, our approach obtains 84% accuracy.
Original languageEnglish
Title of host publicationCoordination, Organizations, Institutions, Norms, and Ethics for Governance of Multi-Agent Systems XIII
Subtitle of host publicationCOIN COINE 2017 2020
EditorsA Aler Tubella, S Cranefield, C Frantz, F Meneguzzi, W Vasconcelos
ISBN (Print)978-3-030-72375-0
Publication statusPublished - 2 Apr 2021

Publication series

NameLecture Notes in Computer Science

Bibliographical note


We gratefully thank Google Research Awards for Latin America for funding our project.


  • Norms
  • Contracts
  • Deep learning
  • Natural language


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