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Publication Detail
Machine Understandable Contracts with Deep Learning
  • Publication Type:
    Conference
  • Authors:
    Dolga R, Treleaven P, Denny MT
  • Publication date:
    23/06/2021
  • Pagination:
    551, 557
  • Published proceedings:
    Proceedings - 2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020
  • ISBN-13:
    9781728176246
  • Status:
    Published
  • Name of conference:
    2020 International Conference on Computational Science and Computational Intelligence (CSCI)
Abstract
This research investigates the automatic translation of contracts to computer understandable rules trough Natural Language Processing. The most challenging aspect, which is studied throughout this paper, is to understand the meaning of the contract and express it into a structured format. This problem can be reduced to the Named Entity Recognition and Rule Extraction tasks, the latter handles the extraction of terms and conditions. These two problems are difficult, but deep learning models can tackle them. We think that this paper is the first work to approach Rule Extraction with deep learning. This method is data-hungry, so the research also introduces data sets for these two tasks. Additionally, it contributes to the literature by introducing Law-Bert, a model based on BERT which is pre-trained on unlabelled contracts. The results obtained on Named Entity Recognition and Rule Extraction show that pre-training on contracts has a positive effect on performance for the downstream tasks.
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