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Register a new userDiscovering Explanatory Sentences in Legal Case Decisions Using Pre-trained Language Models
اكتشاف الجمل التوضيحية في قرارات الحالة القانونية باستخدام نماذج اللغة المدربة مسبقا
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النصوص القانونية تستخدم بشكل روتيني المفاهيم التي يصعب فهمها.يعتمد المحامون على معنى هذه المفاهيم من جانب أمور أخرى، والتحقيق بعناية في كيفية استخدامها في الماضي.العثور على قصاصات نصية تذكر مفهوم معين بطريقة مفيدة ومملة واسعة من الوقت، وبالتالي مكلفة.لقد جمعنا مجموعة بيانات قدرها 26،959 جمل، من قرارات القضية القانونية، وعلقتهم من حيث فائدتهم لشرح مفاهيم قانونية مختارة.باستخدام DataSet نقوم بدراسة فعالية نماذج المحولات المدربة مسبقا على لغة بلغة كبيرة للكشف عن أي من الجمل مفيدة.في ضوء تنبؤات النماذج، نقوم بتحليل الخصائص اللغوية المختلفة للجمل التوضيحية وكذلك علاقتها بالمفهوم القانوني الذي يجب تفسيره.نظهر أن النماذج القائمة على المحولات قادرة على تعلم ميزات متطورة بشكل مدهش وتتفوق على النهج المسبقة للمهمة.
Legal texts routinely use concepts that are difficult to understand. Lawyers elaborate on the meaning of such concepts by, among other things, carefully investigating how they have been used in the past. Finding text snippets that mention a particular concept in a useful way is tedious, time-consuming, and hence expensive. We assembled a data set of 26,959 sentences, coming from legal case decisions, and labeled them in terms of their usefulness for explaining selected legal concepts. Using the dataset we study the effectiveness of transformer models pre-trained on large language corpora to detect which of the sentences are useful. In light of models' predictions, we analyze various linguistic properties of the explanatory sentences as well as their relationship to the legal concept that needs to be explained. We show that the transformer-based models are capable of learning surprisingly sophisticated features and outperform the prior approaches to the task.
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