2021
EMNLP
EMNLP 2021
Few-shot and Zero-shot Approaches to Legal Text Classification: A Case Study in the Financial Sector
Abstract
AbstractThe application of predictive coding techniques to legal texts has the potential to greatly reduce the cost of legal review of documents, however, there is such a wide array of legal tasks and continuously evolving legislation that it is hard to construct sufficient training data to cover all cases. In this paper, we investigate few-shot and zero-shot approaches that require substantially less training data and introduce a triplet architecture, which for promissory statements produces performance close to that of a supervised system. This method allows predictive coding methods to be rapidly developed for new regulations and markets.
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Interdisciplinary Bridge
— Deep Learning and Machine Learning and Natural Language Processing
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Keyword Pioneer
— triplet architecture
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Hot Topic Early Bird
— legal text
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Authors
Topics
Machine Learning > Learning Types > Zero-Shot Learning
Natural Language Processing > Applications > Text Classification
Machine Learning > Learning Paradigms > Few-Shot Learning
Machine Learning > Learning Types > Few-Shot Learning
Machine Learning > Learning Paradigms > Zero-Shot Learning
Deep Learning > Learning Types > Zero-Shot Learning
Deep Learning > Learning Types > Few-Shot Learning