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 language | English |
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Title of host publication | 18th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2019 |
Publisher | International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS) |
Pages | 1764-1766 |
Number of pages | 3 |
ISBN (Electronic) | 9781510892002 |
Publication status | Published - 2019 |
Event | 18th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2019 - Montreal, Canada Duration: 13 May 2019 → 17 May 2019 |
Conference
Conference | 18th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2019 |
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Country/Territory | Canada |
City | Montreal |
Period | 13/05/19 → 17/05/19 |
Bibliographical note
Publisher Copyright:© 2019 International Foundation for Autonomous Agents and Multiagent Systems. All rights reserved.