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Register a new userDetecting Speaker Personas from Conversational Texts
الكشف عن شخصيات المتكلم من نصوص المحادثة
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شخصية مفيدة لتنبؤ استجابة الحوار. ومع ذلك، فإن الشخصية المستخدمة في الدراسات الحالية محددة مسبقا ويصعب الحصول عليها قبل محادثة. لمعالجة هذه المشكلة، نقوم بدراسة مهمة جديدة، اسمه مكبر صوت مكبر الصوت (SPD)، الذي يهدف إلى اكتشاف شخصيات المتكلم بناء على نص المحادثة العادي. في هذه المهمة، يتم تفتيش شخص أفضل مطابقة من المرشحين بالنظر إلى نص المحادثة. هذه مهمة مطابقة دهالية متعددة إلى العديد لأن كل من السياقات والشخصية في SPD تتكون من جمل متعددة. يعزز التبعية الطويلة الأجل والتكرار الديناميكي بين هذه الجمل صعوبة هذه المهمة. نحن نبني مجموعة بيانات ل SPD، التي يطلق عليها مواضيع شخصيا على أخصائي الدردشة (PMPC). علاوة على ذلك، نقيم العديد من النماذج الأساسية واقتراح شبكات مطابقة الكلام إلى الملف الشخصي (U2P) لهذه المهمة. تعمل نماذج U2P بتصبيح جيد يعالج كل من السياقات والعشرون كمجموعات من تسلسل متعددة. بعد ذلك، يتم تسجيل كل زوج تسلسل ويتم الحصول على درجة إجمالية قابلة للتفسير للحصول على زوج سياق شخصي من خلال التجميع. تظهر نتائج التقييم أن نماذج U2P تتفوق على نظرائهم الأساسيين بشكل كبير.
Personas are useful for dialogue response prediction. However, the personas used in current studies are pre-defined and hard to obtain before a conversation. To tackle this issue, we study a new task, named Speaker Persona Detection (SPD), which aims to detect speaker personas based on the plain conversational text. In this task, a best-matched persona is searched out from candidates given the conversational text. This is a many-to-many semantic matching task because both contexts and personas in SPD are composed of multiple sentences. The long-term dependency and the dynamic redundancy among these sentences increase the difficulty of this task. We build a dataset for SPD, dubbed as Persona Match on Persona-Chat (PMPC). Furthermore, we evaluate several baseline models and propose utterance-to-profile (U2P) matching networks for this task. The U2P models operate at a fine granularity which treat both contexts and personas as sets of multiple sequences. Then, each sequence pair is scored and an interpretable overall score is obtained for a context-persona pair through aggregation. Evaluation results show that the U2P models outperform their baseline counterparts significantly.
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