قياس الحدث أمر ضروري في فهم القصص.تأخذ هذه الورقة طريقة غير مخالفة مؤخرا للكشف عن الصيغة المستمدة من القارب الكاردينال ونظريات مفاجأة وتطبيقها على أشكال سردية أطول.نحن نحسن نموذج لغة المحولات القياسية من خلال دمج قاعدة معرفة خارجية (مشتقة من توليد استرجاع المعزز) وإضافة آلية ذاكرة لتعزيز الأداء في أعمال أطول.نحن نستخدم نهج رواية لاستخلاص شرح Salience باستخدام ملخصات الفصل الانحياز من شمس كوربوس للأعمال الأدبية الكلاسيكية.يوضح تقييمنا ضد هذه البيانات أن نموذج الكشف عن ملحقاتنا يحسن الأداء فوقه ونموذج اللغة غير المعدلة والذاكرة، وكلاهما ضروري لهذا التحسن.
Measuring event salience is essential in the understanding of stories. This paper takes a recent unsupervised method for salience detection derived from Barthes Cardinal Functions and theories of surprise and applies it to longer narrative forms. We improve the standard transformer language model by incorporating an external knowledgebase (derived from Retrieval Augmented Generation) and adding a memory mechanism to enhance performance on longer works. We use a novel approach to derive salience annotation using chapter-aligned summaries from the Shmoop corpus for classic literary works. Our evaluation against this data demonstrates that our salience detection model improves performance over and above a non-knowledgebase and memory augmented language model, both of which are crucial to this improvement.
References used
https://aclanthology.org/
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