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Register a new userEntity and Evidence Guided Document-Level Relation Extraction
التركيب والدليل استخراج العلاقات على مستوى المستند
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إن استخراج العلاقات على مستوى المستند هو مهمة صعبة، تتطلب التفكير في جمل متعددة للتنبؤ بمجموعة من العلاقات في وثيقة.في هذه الورقة، نقترح إطار رواية E2GRE (الكيان والأدلة استخراج التعادل الموجود) التي تستخرج العلاقات بشكل مشترك وعمليات الأدلة الأساسية باستخدام نموذج اللغة المسبق الكبير (LM) كمشفر مدخلات.أولا، نقترح توجيه آلية انتباه LM مسبقا للتركيز على السياق ذي الصلة باستخدام احتمالات الاهتمام كميزات إضافية لتنبؤ الأدلة.علاوة على ذلك، بدلا من إطعام المستند بأكمله إلى LMS محداس للحصول على تمثيل كيان، نسلسل نص المستندات مع كيانات رئيسية للمساعدة في تركيز LMS على أجزاء من الوثيقة التي ترتبط أكثر بكيان الرأس.تتعلم E2GRE لدينا بشكل مشترك استخراج العلاقة والتنبؤ بالأدلة بفعالية، مما يدل على مكاسب كبيرة على كل من هذه المهام، والتي نجدها مرتبطة بشدة.
Document-level relation extraction is a challenging task, requiring reasoning over multiple sentences to predict a set of relations in a document. In this paper, we propose a novel framework E2GRE (Entity and Evidence Guided Relation Extraction) that jointly extracts relations and the underlying evidence sentences by using large pretrained language model (LM) as input encoder. First, we propose to guide the pretrained LM's attention mechanism to focus on relevant context by using attention probabilities as additional features for evidence prediction. Furthermore, instead of feeding the whole document into pretrained LMs to obtain entity representation, we concatenate document text with head entities to help LMs concentrate on parts of the document that are more related to the head entity. Our E2GRE jointly learns relation extraction and evidence prediction effectively, showing large gains on both these tasks, which we find are highly correlated.
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Document-level relation extraction aims to identify relations between entities in a whole document. Prior efforts to capture long-range dependencies have relied heavily on implicitly powerful representations learned through (graph) neural networks, w
hich makes the model less transparent. To tackle this challenge, in this paper, we propose LogiRE, a novel probabilistic model for document-level relation extraction by learning logic rules. LogiRE treats logic rules as latent variables and consists of two modules: a rule generator and a relation extractor. The rule generator is to generate logic rules potentially contributing to final predictions, and the relation extractor outputs final predictions based on the generated logic rules. Those two modules can be efficiently optimized with the expectation-maximization (EM) algorithm. By introducing logic rules into neural networks, LogiRE can explicitly capture long-range dependencies as well as enjoy better interpretation. Empirical results show that significantly outperforms several strong baselines in terms of relation performance and logical consistency. Our code is available at https://github.com/rudongyu/LogiRE.
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