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Register a new userCO-NNECT: A Framework for Revealing Commonsense Knowledge Paths as Explicitations of Implicit Knowledge in Texts
Nnect Co-Nnect: إطار للكشف عن مسارات معرفة المنطقية كصفات عن المعرفة الضمنية في النصوص
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في هذا العمل، نستفيد المعرفة المنوية في شكل مسارات المعرفة لإقامة صلات بين الجمل، كشكل من أشكال تفسير المعرفة الضمنية. يمكن أن تكون هذه الاتصالات مباشرة (مسارات مفردة) أو تتطلب مفاهيم وسيطة (مسارات Multihop). لبناء مثل هذه المسارات تجمع بين نوعين نموذجيين في إطار مشترك نسميه Nnect: مصنف علاقة يتنبأ بالاتصالات المباشرة بين المفاهيم؛ ونموذج التنبؤ المستهدف الذي يولد مفاهيم مستهدفة أو متوسطة بالنظر إلى مفهوم مصدر وعلاقة، والتي نستخدمها لإنشاء مسارات Multihop. على عكس العمل السابق الذي يعتمد بشكل حصري على مصادر المعرفة الثابتة، فإننا نستفيد من نماذج اللغة المصنعة للمعرفة المخزنة في Congalnet، لتوليد مسارات المعرفة بشكل حيوي، كشروح من المعرفة الضمنية التي تربط الجمل في النصوص. كمساهمة مركزية نقوم بتصميم إعدادات التقييم اليدوية والآلية لتقييم جودة المسارات التي تم إنشاؤها. نقوم بإجراء تقييمات على رقمين جدليين وإظهار أن هناك مزيج من النوعين النموذجيين يولد مسارات معارف ذات مغزى وعالية الجودة بين الجمل التي تكشف عن المعرفة الضمنية المنقولة في النص.
In this work we leverage commonsense knowledge in form of knowledge paths to establish connections between sentences, as a form of explicitation of implicit knowledge. Such connections can be direct (singlehop paths) or require intermediate concepts (multihop paths). To construct such paths we combine two model types in a joint framework we call Co-nnect: a relation classifier that predicts direct connections between concepts; and a target prediction model that generates target or intermediate concepts given a source concept and a relation, which we use to construct multihop paths. Unlike prior work that relies exclusively on static knowledge sources, we leverage language models finetuned on knowledge stored in ConceptNet, to dynamically generate knowledge paths, as explanations of implicit knowledge that connects sentences in texts. As a central contribution we design manual and automatic evaluation settings for assessing the quality of the generated paths. We conduct evaluations on two argumentative datasets and show that a combination of the two model types generates meaningful, high-quality knowledge paths between sentences that reveal implicit knowledge conveyed in text.
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