عند قراءة قطعة أدبية، غالبا ما يصنع القراء استنتاجات حول أدوار الشخصيات والشخصيات والعلاقات والمهالية والإجراءات، وما إلى ذلك بينما يمكن للبشر السحب بسهولة على تجاربهم السابقة لبناء مثل هذه النظرة التي تركز على الطابع للسرد، فهم الشخصياتيمكن أن تكون الروايات مهمة صعبة للأجهزة.لتشجيع البحث في هذا المجال من فهم السرد المركزي بالشخصية، نقدم LCSU - مجموعة بيانات جديدة من القطع الأدبية وملخصاتها مقترن بأوصاف الشخصيات التي تظهر فيها.نقدم أيضا مهام جديدة على LCCU: تحديد الأحرف وتوليد وصف الشخصيات.تجاربنا مع العديد من النماذج اللغوية المدربة مسبقا مكيفة لهذه المهام توضح أن هناك حاجة إلى نماذج أفضل من الفهم السردي.
When reading a literary piece, readers often make inferences about various characters' roles, personalities, relationships, intents, actions, etc. While humans can readily draw upon their past experiences to build such a character-centric view of the narrative, understanding characters in narratives can be a challenging task for machines. To encourage research in this field of character-centric narrative understanding, we present LiSCU -- a new dataset of literary pieces and their summaries paired with descriptions of characters that appear in them. We also introduce two new tasks on LiSCU: Character Identification and Character Description Generation. Our experiments with several pre-trained language models adapted for these tasks demonstrate that there is a need for better models of narrative comprehension.
References used
https://aclanthology.org/
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