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Register a new userFew-shot and Zero-shot Approaches to Legal Text Classification: A Case Study in the Financial Sector
طرق قليلة من الأساطير والصفرية لتصنيف النص القانوني: دراسة حالة في القطاع المالي
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إن تطبيق تقنيات الترميز التنبؤية للنصوص القانونية لديه القدرة على تقليل تكلفة المراجعة القانونية للوثائق، ومع ذلك، هناك مثل هذه المجموعة الواسعة من المهام القانونية والتشريعات المتطورة باستمرار من الصعب بناء بيانات تدريبية كافية لتغطية جميعهاحالات.في هذه الورقة، نقوم بالتحقيق في طرق قليلة من الأساطير والرصاص التي تتطلب بيانات تدريب أقل بكثير وإدخال هندسة ثلاثية، والتي تنتج البيانات الإذنية أداء قريبة من نظام نظام إشرافي.تسمح هذه الطريقة بطرق ترميز التنبؤ أن يتم تطويرها بسرعة للوائح والأسواق الجديدة.
The 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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