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Register a new userEverything Has a Cause: Leveraging Causal Inference in Legal Text Analysis
كل شيء له سبب: الاستفادة من الاستدلال السببية في تحليل النص القانوني
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الاستدلال السببية هو عملية التقاط علاقة تأثير السبب بين المتغيرات.تركز معظم الأعمال الموجودة على التعامل مع البيانات المنظمة، أثناء التعدين العلاقة السببية بين عوامل البيانات غير المنظمة، مثل النص، أقل فحصا، ولكنها ذات أهمية كبيرة، خاصة في المجال القانوني.في هذه الورقة، نقترح إطار الاستدلال السببية المستندة إلى الرسم البياني (GCI) على الرواية، والذي يبني الرسوم البيانية السببية من أوصاف الحقائق دون تورط إنساني كبير ويمكن الاستدلال السببية لتسهيل الممارسين القانونيين لإجراء قرارات مناسبة.نقيم الإطار على مهمة تحدي مهمة غموض مماثلة.تظهر النتائج التجريبية أن GCI يمكن أن تلتقط نفاد الفقراء من أوصاف الحقائق بين رسوم مربكة متعددة وتوفير تمييز قابل للتفسير، وخاصة في إعدادات قليلة.نلاحظ أيضا أن المعرفة السببية الواردة في GCI يمكن حقنها بشكل فعال في شبكات عصبية قوية لتحسين الأداء والتفسيرية.
Causal inference is the process of capturing cause-effect relationship among variables. Most existing works focus on dealing with structured data, while mining causal relationship among factors from unstructured data, like text, has been less examined, but is of great importance, especially in the legal domain. In this paper, we propose a novel Graph-based Causal Inference (GCI) framework, which builds causal graphs from fact descriptions without much human involvement and enables causal inference to facilitate legal practitioners to make proper decisions. We evaluate the framework on a challenging similar charge disambiguation task. Experimental results show that GCI can capture the nuance from fact descriptions among multiple confusing charges and provide explainable discrimination, especially in few-shot settings. We also observe that the causal knowledge contained in GCI can be effectively injected into powerful neural networks for better performance and interpretability.
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