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Register a new userCSAGN: Conversational Structure Aware Graph Network for Conversational Semantic Role Labeling
CSAGN: هيكل المحادثة تدرك شبكة الرسم البياني لعلامات الدور الدلالي المحادثة
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ويعتقد أن وضع العلامات الدلالية الدلالية للمحادثة (CSRL) هي خطوة حاسمة نحو فهم الحوار.ومع ذلك، لا يزال يمثل تحديا كبيرا لمحلل CSRL الحالي للتعامل مع المعلومات الهيكلية للمحادثة.في هذه الورقة، نقدم بنية بسيطة وفعالة ل CSRL التي تهدف إلى معالجة هذه المشكلة.يعتمد نموذجنا على شبكة الرسم البياني على بنية المحادثة التي تشفصها بشكل صريح لمعلومات مكبر الصوت.نقترح أيضا طريقة تعليمية متعددة المهام لمواصلة تحسين النموذج.تظهر النتائج التجريبية على مجموعات البيانات القياسية أن نموذجنا مع أهداف التدريب المقترحة لدينا تتفوق بشكل كبير على الأساس السابقة.
Conversational semantic role labeling (CSRL) is believed to be a crucial step towards dialogue understanding. However, it remains a major challenge for existing CSRL parser to handle conversational structural information. In this paper, we present a simple and effective architecture for CSRL which aims to address this problem. Our model is based on a conversational structure aware graph network which explicitly encodes the speaker dependent information. We also propose a multi-task learning method to further improve the model. Experimental results on benchmark datasets show that our model with our proposed training objectives significantly outperforms previous baselines.
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