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Register a new userDiscussion Structure Prediction Based on a Two-step Method
تنبؤ بنية المناقشة بناء على طريقة خطوتين
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المحادثات غالبا ما تكون في المختبرات والشركات.ملخص أمر حيوي لفهم محتوى مناقشة للأشخاص الذين لم يحضروا المناقشة.إذا تم توضيح الملخص كهيكل وسيطة، فمن المفيد فهم أساسيات المناقشة على الفور.هدفنا في هذه الورقة هو التنبؤ بهيكل رابط بين العقد التي تتكون من الكلام في محادثة: تصنيف كل زوج عقدة إلى مرتبط "أو غير مرتبط." نهج واحد للتنبؤ به الهيكل هو استخدام نماذج تعلم الآلات.ومع ذلك، فإن النتيجة تميل إلى الإفراط في توليد روابط العقد.لحل هذه المشكلة، نقدم طريقة من خطوتين لمهمة التنبؤ الهيكل.نحن نستخدم نهج تعتمد على الجهاز كخطوة الأولى: مهمة تنبؤ الرابط.بعد ذلك، نطبق نهجا يستند إلى النتيجة كخطوة ثانية: مهمة اختيار الارتباط.تحسنت أساليبنا من خطوتين بشكل كبير الدقة مقارنة بطرق خطوة واحدة تستند إلى SVM و BERT.
Conversations are often held in laboratories and companies. A summary is vital to grasp the content of a discussion for people who did not attend the discussion. If the summary is illustrated as an argument structure, it is helpful to grasp the discussion's essentials immediately. Our purpose in this paper is to predict a link structure between nodes that consist of utterances in a conversation: classification of each node pair into linked'' or not-linked.'' One approach to predict the structure is to utilize machine learning models. However, the result tends to over-generate links of nodes. To solve this problem, we introduce a two-step method to the structure prediction task. We utilize a machine learning-based approach as the first step: a link prediction task. Then, we apply a score-based approach as the second step: a link selection task. Our two-step methods dramatically improved the accuracy as compared with one-step methods based on SVM and BERT.
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