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Register a new userDiscrete Argument Representation Learning for Interactive Argument Pair Identification
تمثيل جدال منفصل التعلم لتحديد زوج الحجة التفاعلية
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في هذه الورقة، نركز على تحديد أزواج الحجة التفاعلية من وظيفتين مع مواقف معاكسة إلى موضوع معين.النظر في الآراء يتم تبادلها من وجهات نظر مختلفة موضوع المناقشة، ندرس تمثيلات منفصلة للحجج لالتقاط جوانب متفاوتة في لغات الحال (على سبيل المثال، التركيز من النقاش وسلوك المشارك).علاوة على ذلك، فإننا نستخدم الهيكل الهرمي لنموذج المعلومات بعد الحكمة التي تتضمن المعرفة السياقية.تظهر النتائج التجريبية على مجموعة البيانات الواسعة النطاق التي تم جمعها من CMV أن إطار عملنا المقترح قد يتفوق بشكل كبير على خطوط الأساس التنافسية.تكشف التحليلات الإضافية لماذا ينتج نموذجنا أداء متفوقا وأثبت فائدة تمثيلاتنا المستفادة.
In this paper, we focus on identifying interactive argument pairs from two posts with opposite stances to a certain topic. Considering opinions are exchanged from different perspectives of the discussing topic, we study the discrete representations for arguments to capture varying aspects in argumentation languages (e.g., the debate focus and the participant behavior). Moreover, we utilize hierarchical structure to model post-wise information incorporating contextual knowledge. Experimental results on the large-scale dataset collected from CMV show that our proposed framework can significantly outperform the competitive baselines. Further analyses reveal why our model yields superior performance and prove the usefulness of our learned representations.
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