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Weakly Supervised One-shot Classification using Recurrent Neural Networks with Attention: Application to Claim Acceptance Detection

Abstract : Determining if a claim is accepted given judge arguments is an important non-trivial task in court decisions analyses. Application of recent efficient machine learning techniques may however be inappropriate for tackling this problem since, in the Legal domain, labelled datasets are most often small, scarce and expensive. This paper presents a deep learning model and a methodology for solving such complex classification tasks with only few labelled examples. We show in particular that mixing one-shot learning with recurrent neural networks and an attention mechanism enables obtaining efficient models while preserving some form of inter-pretability and limiting potential overfit. Results obtained on several types of claims in French court decisions, using different vectorization processes, are presented.
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https://hal.archives-ouvertes.fr/hal-02407405
Contributor : Sébastien Harispe <>
Submitted on : Thursday, December 12, 2019 - 3:00:55 PM
Last modification on : Wednesday, June 24, 2020 - 4:18:15 PM
Long-term archiving on: : Friday, March 13, 2020 - 9:54:28 PM

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Charles Condevaux, Sébastien Harispe, Stéphane Mussard, Guillaume Zambrano. Weakly Supervised One-shot Classification using Recurrent Neural Networks with Attention: Application to Claim Acceptance Detection. JURIX 2019 32nd International Conference on Legal Knowledge and Information Systems, Dec 2019, Madrid, Spain. ⟨hal-02407405⟩

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