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Register a new userEffectively Leveraging BERT for Legal Document Classification
الاستفادة الفعالة بيرت تصنيف الوثيقة القانونية
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حقق تمثيلات تشفير ثنائية الاتجاه من المحولات (بيرت) عروضا حديثة على العديد من مهام تصنيف النص، مثل تحليل الغراء والمعنويات. بدأ العمل الأخير في المجال القانوني في استخدام بيرت في المهام، مثل التنبؤ بالحكم القانوني والتنبؤ بالانتهاك. تتمثل الممارسات الشائعة في استخدام Bert في ضبط نموذج مدرب مسبقا على مهمة مستهدفة واقتطع نصات الإدخال بحجم إدخال BERT (E.G. في معظم الرموز 512). ومع ذلك، نظرا للخصائص الفريدة للمستندات القانونية، فليس من الواضح كيفية التكيف بفعالية بيرت في المجال القانوني. في هذا العمل، نحقق في كيفية التعامل مع المستندات الطويلة، وكيفية أهمية التدريب المسبق على المستندات من نفس المجال باعتباره المهمة المستهدفة. نحن نقوم بإجراء تجارب على مجموعات البيانات الأخيرة: DataSet DataSet ECHR ومجموعة بيانات المهام الناجحة، وهي مهام التصنيف متعددة الملصقات والتصنيف الثنائي، على التوالي. الأهم من ذلك، في المتوسط عدد الرموز في وثيقة من مجموعة بيانات انتهاك EURR أكثر من 1600. في حين أن المستندات الموجودة في مجموعة بيانات المهام الأساسية أقصر (الحد الأقصى لعدد الرموز 204). قارننا بدقة العديد من التقنيات لتكييف بيرت على وثائق طويلة ومقارنة النماذج المختلفة المدربة مسبقا على المجالات القانونية وغيرها. تظهر نتائجنا التجريبية أننا نحتاج إلى التكيف بشكل صريح بتكييف بيرت للتعامل مع المستندات الطويلة، حيث يؤدي اقتطاع إلى أداء أقل فعالية. وجدنا أيضا أن التدريب المسبق على المستندات التي تشبه المهمة المستهدفة ستؤدي إلى أداء أكثر فعالية في العديد من السيناريو.
Bidirectional Encoder Representations from Transformers (BERT) has achieved state-of-the-art performances on several text classification tasks, such as GLUE and sentiment analysis. Recent work in the legal domain started to use BERT on tasks, such as legal judgement prediction and violation prediction. A common practise in using BERT is to fine-tune a pre-trained model on a target task and truncate the input texts to the size of the BERT input (e.g. at most 512 tokens). However, due to the unique characteristics of legal documents, it is not clear how to effectively adapt BERT in the legal domain. In this work, we investigate how to deal with long documents, and how is the importance of pre-training on documents from the same domain as the target task. We conduct experiments on the two recent datasets: ECHR Violation Dataset and the Overruling Task Dataset, which are multi-label and binary classification tasks, respectively. Importantly, on average the number of tokens in a document from the ECHR Violation Dataset is more than 1,600. While the documents in the Overruling Task Dataset are shorter (the maximum number of tokens is 204). We thoroughly compare several techniques for adapting BERT on long documents and compare different models pre-trained on the legal and other domains. Our experimental results show that we need to explicitly adapt BERT to handle long documents, as the truncation leads to less effective performance. We also found that pre-training on the documents that are similar to the target task would result in more effective performance on several scenario.
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