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Register a new userLogic-level Evidence Retrieval and Graph-based Verification Network for Table-based Fact Verification
شبكة تسجيل الأدلة على مستوى المنطق وشبكة التحقق المستندة إلى الرسم البياني للتحقق من الحقائق المستندة إلى الجدول
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تهدف مهمة التحقق من الحقائق القائمة على الطاولة إلى التحقق مما إذا كان البيان المحدد مدعوم من الجدول شبه المنظم المحدد. يلعب المنطق الرمزي مع العمليات المنطقية دورا حاسما في هذه المهمة. الأساليب الحالية الاستفادة من البرامج التي تحتوي على معلومات منطقية غنية لتعزيز عملية التحقق. ومع ذلك، نظرا لعدم وجود إشارات خاضعة للإشراف بالكامل في عملية توليد البرنامج، يمكن استخلاص البرامج الزائفة وعملها، مما يؤدي إلى عدم قدرة النموذج على العمليات المنطقية المفيدة. لمعالجة المشكلات المذكورة أعلاه، في هذا العمل، نقوم بصياغة مهمة التحقق من الحقائق القائمة على الطاولة كإطار لاسترجاع الأدلة والتفكير، حيث اقترح شبكة التحقق من الأدلة على مستوى المنطق وشبكة التحقق القائمة على الرسم البياني (LERGV). على وجه التحديد، نقوم أولا باسترجئة الأدلة التي تشبه البرامج على مستوى المنطق من الجدول المعطى والبيان كدليل تكميلي على الطاولة. بعد ذلك، نقوم بإنشاء رسم بياني لمستوى منطقي لالتقاط العلاقات المنطقية بين الكيانات والوظائف في الأدلة المستردة، وتصميم شبكة التحقق القائمة على الرسم البياني لإجراء المنطق المستندة إلى الرسم البياني على مستوى المنطق بناء على الرسم البياني الذي تم إنشاؤه لتصنيف النهائي علاقة استقامة. النتائج التجريبية على Tabract Tabract القياسي على نطاق واسع تظهر فعالية النهج المقترح.
Table-based fact verification task aims to verify whether the given statement is supported by the given semi-structured table. Symbolic reasoning with logical operations plays a crucial role in this task. Existing methods leverage programs that contain rich logical information to enhance the verification process. However, due to the lack of fully supervised signals in the program generation process, spurious programs can be derived and employed, which leads to the inability of the model to catch helpful logical operations. To address the aforementioned problems, in this work, we formulate the table-based fact verification task as an evidence retrieval and reasoning framework, proposing the Logic-level Evidence Retrieval and Graph-based Verification network (LERGV). Specifically, we first retrieve logic-level program-like evidence from the given table and statement as supplementary evidence for the table. After that, we construct a logic-level graph to capture the logical relations between entities and functions in the retrieved evidence, and design a graph-based verification network to perform logic-level graph-based reasoning based on the constructed graph to classify the final entailment relation. Experimental results on the large-scale benchmark TABFACT show the effectiveness of the proposed approach.
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