Contracts formally represent agreements between parties and often involve the exchange of goods and services. In contracts, norms define the expected behaviors from parties using deontic statements, such as obligations, permissions, and prohibitions. However, norms may conflict when two or more apply to the same context but have different deontic statements, such as a permission to delay payment being present in the same contract as an obligation to pay in a fixed deadline. Contracts with conflicting norms may be invalidated in whole or in part, making conflict identification a major concern in contract writing. Conflict identification in contracts by humans is a time-consuming and error-prone task that would greatly benefit from automated aid. In order to automate such identification, we introduce an approach to identify potential conflicts between norms in contracts written in natural language that compares a latent (vector) representation of norms using an implicit offset that encodes normative conflicts. Experimental evaluation shows that our approach is substantially more accurate than the existing state of the art in an open dataset.
|Title of host publication||2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Publication status||Published - 10 Oct 2018|
|Event||2018 International Joint Conference on Neural Networks, IJCNN 2018 - Rio de Janeiro, Brazil|
Duration: 8 Jul 2018 → 13 Jul 2018
|Name||Proceedings of the International Joint Conference on Neural Networks|
|Conference||2018 International Joint Conference on Neural Networks, IJCNN 2018|
|City||Rio de Janeiro|
|Period||8/07/18 → 13/07/18|
Bibliographical noteFunding Information:
The authors would like to thank CNPq and NVIDIA for funding this research.
© 2018 IEEE.