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Register a new userSelf- and Pseudo-self-supervised Prediction of Speaker and Key-utterance for Multi-party Dialogue Reading Comprehension
التنبؤ الذاتي والإشراف الذاتي للإشراف على نفسه من المتكلم والنظر الرئيسي لفهم قراءة الحوار متعدد الأحزاب
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يجلب الفهم القراءة آلة حوار متعدد الأحزاب (MRC) تحديا هائلا لأنه ينطوي على مكبرات صوت متعددة في حوار واحد، مما أدى إلى تدفقات معلومات المتكلم المعقدة وسياقات الحوار الصاخبة.لتخفيف هذه الصعوبات، تركز النماذج السابقة على كيفية دمج هذه المعلومات باستخدام الوحدات النمطية المستندة إلى الرسم البياني المعقدة والبيانات الإضافية المسمى يدويا، والتي عادة ما تكون نادرة في السيناريوهات الحقيقية.في هذه الورقة، نقوم بتصميم مهام التنبؤ ذات الإشراف الذاتي والخالية من العمالة في العمل على المتكلم والكلام الرئيسي للنموذج الضمني لتدفقات معلومات المتكلم، والتقاط أدلة بارزة في حوار طويل.تبرر النتائج التجريبية على مجموعة من مجموعات البيانات القياسية لفعالية أسلوبنا على أساس الأساس التنافسي والنماذج الحديثة الحالية.
Multi-party dialogue machine reading comprehension (MRC) brings tremendous challenge since it involves multiple speakers at one dialogue, resulting in intricate speaker information flows and noisy dialogue contexts. To alleviate such difficulties, previous models focus on how to incorporate these information using complex graph-based modules and additional manually labeled data, which is usually rare in real scenarios. In this paper, we design two labour-free self- and pseudo-self-supervised prediction tasks on speaker and key-utterance to implicitly model the speaker information flows, and capture salient clues in a long dialogue. Experimental results on two benchmark datasets have justified the effectiveness of our method over competitive baselines and current state-of-the-art models.
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