Classification of contractual conflicts via learning of semantic representations

Joao Paulo Aires, Roger Granada, Juarez Monteiro, Rodrigo Coelho Barros, Felipe Meneguzzi

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

1 Citation (Scopus)

Abstract

Contracts are the main medium through which parties formalize their trade relations, be they the exchange of goods or the specification of mutual obligations. While electronic contracts allow automated processes to verify their correctness, most agreements in the real world are still written in natural language, which need substantial human revision effort to eliminate possible conflicting statements in long and complex contracts. In this paper, we formalize a typology of conflict types between clauses suitable for machine learning and develop techniques to review contracts by learning to identify and classify such conflicts, facilitating the task of contract revision. We evaluate the effectiveness of our techniques using a manually annotated contract conflict corpus with results close to the current state-of-the-art for conflict identification, while introducing a more complex classification task of such conflicts for which our method surpasses the state-of-the art method.,Natural Language Processing; Norms; Norm Conflicts; Semantic Representation.

Original languageEnglish
Title of host publication18th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2019
PublisherInternational Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
Pages1764-1766
Number of pages3
ISBN (Electronic)9781510892002
Publication statusPublished - 2019
Event18th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2019 - Montreal, Canada
Duration: 13 May 201917 May 2019

Conference

Conference18th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2019
Country/TerritoryCanada
CityMontreal
Period13/05/1917/05/19

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