Norm Conflict Identification using Deep Learning

João Paulo Aires, Felipe Meneguzzi

Research output: Chapter in Book/Report/Conference proceedingPublished conference contribution

7 Citations (Scopus)

Abstract

Contracts represent agreements between two or more parties formally in the form of deontic statements or norms within their clauses. If not carefully designed, such conflicts may invalidate an entire contract, and thus 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 deep learning to extract and compare norms in order to identify conflicts between them. Using a manually annotated set of conflicts as train and test set, our approach obtains 85% accuracy, establishing a new state-of-the art.
Original languageEnglish
Title of host publicationAutonomous Agents and Multiagent Systems. AAMAS 2017
EditorsG Sukthankar, J Rodriguez-Aguilar
PublisherSpringer
Pages194-207
Number of pages14
DOIs
Publication statusPublished - Nov 2017

Publication series

NameLecture Notes in Computer Science
Volume10643

Bibliographical note

Aires, J.P., Meneguzzi, F. (2017). Norm Conflict Identification Using Deep Learning. In: Sukthankar, G., Rodriguez-Aguilar, J. (eds) Autonomous Agents and Multiagent Systems. AAMAS 2017. Lecture Notes in Computer Science(), vol 10643. Springer, Cham.

International Conference on Autonomous Agents and Multiagent Systems: AAMAS 2017: Autonomous Agents and Multiagent Systems pp 194–207

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

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