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Register a new userTelling Stories through Multi-User Dialogue by Modeling Character Relations
سرد القصص من خلال حوار متعدد المستخدمين عن طريق نمذجة العلاقات الشخصية
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تستكشف هذه الورقة استمرار القصة التي يحركها الشخصية، حيث تظهر القصة من خلال سرد الشخصيات الأول والثاني بالإضافة إلى الحوار - - - - تتطلب النماذج لتحديد اللغة التي تتفق مع شخصيات الشخصية وعلاقاتها مع أحرف أخرى التالية وتقدم القصة. نحن نفترض أن نموذج متعدد المهام الذي يتدرب على حوار الأحرف بالإضافة إلى معلومات علاقة الشخصية يحسن استمرار القصة المستندة إلى المحولات. تحقيقا لهذه الغاية، نقوم بتوسيع دور محصنة الدوران والتنين الحاسم (Rameshkumar و Bailey، 2020) --- تتكون من نصوص الحوار من الأشخاص الذين يخبرون بشكل تعاظم قصة أثناء لعب لعبة الأبراج المحصنة والتنينات --- مع العلاقات المستخرجة تلقائيا بين كل زوج من الشخصيات التفاعل وكذلك شخصياتهم. تقدم سلسلة من الوضوح دليلا على فرضيتنا، حيث أظهر أن نموذجنا متعدد المهام لدينا باستخدام علاقات الأحرف يحسن دقة استمرار القصة على خطوط خطوط خطوط خطوط خطوط طويلة.
This paper explores character-driven story continuation, in which the story emerges through characters' first- and second-person narration as well as dialogue---requiring models to select language that is consistent with a character's persona and their relationships with other characters while following and advancing the story. We hypothesize that a multi-task model that trains on character dialogue plus character relationship information improves transformer-based story continuation. To this end, we extend the Critical Role Dungeons and Dragons Dataset (Rameshkumar and Bailey, 2020)---consisting of dialogue transcripts of people collaboratively telling a story while playing the role-playing game Dungeons and Dragons---with automatically extracted relationships between each pair of interacting characters as well as their personas. A series of ablations lend evidence to our hypothesis, showing that our multi-task model using character relationships improves story continuation accuracy over strong baselines.
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