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Register a new userContext Tracking Network: Graph-based Context Modeling for Implicit Discourse Relation Recognition
شبكة تتبع السياق: النمذجة السياق المستندة إلى الرسم البياني للاعتراف علاقة الشريط الضمني
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تهدف التعرف على علاقة الخطاب الضمني (IDRR) إلى تحديد العلاقات المنطقية بين جملتين مجاورة في الخطاب.تفشل النماذج الحالية في الاستفادة الكاملة من المعلومات السياقية التي تلعب دورا مهما في تفسير كل جملة محلية.في هذه الورقة، فإننا نقترحنا بالتالي شبكة تتبع السياق في الرسم البياني القائمة على الرسم البياني (شبكة CT) لنموذج سياق الخطاب ل IDRR.تقوم CT-Net أولا بتحويل الخطاب في الرسم البياني لرابطة الفقرة (PAG)، حيث تتبع كل جملة سياقها المرتبطة ارتباطا وثيقا من الخطاب المعقد من خلال أنواع مختلفة من الحواف.بعد ذلك، استخراج CT-NET تمثيل سياقي من PAG من خلال آلية تحديث تم تصميمه خصيصا، مما يمكن أن يدمج بفعالية من كل من دلالات السياق على مستوى الجملة ومستوى الرمز المميز.تشير التجارب على PDTB 2.0 إلى أن شبكة CT-NET أكبر أداء أفضل من النماذج التي نموذجها تقريبا السياق.
Implicit discourse relation recognition (IDRR) aims to identify logical relations between two adjacent sentences in the discourse. Existing models fail to fully utilize the contextual information which plays an important role in interpreting each local sentence. In this paper, we thus propose a novel graph-based Context Tracking Network (CT-Net) to model the discourse context for IDRR. The CT-Net firstly converts the discourse into the paragraph association graph (PAG), where each sentence tracks their closely related context from the intricate discourse through different types of edges. Then, the CT-Net extracts contextual representation from the PAG through a specially designed cross-grained updating mechanism, which can effectively integrate both sentence-level and token-level contextual semantics. Experiments on PDTB 2.0 show that the CT-Net gains better performance than models that roughly model the context.
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