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Register a new userHierarchical Encoders for Modeling and Interpreting Screenplays
الترميز الهرمي للنمذجة وتفسير screenplays
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في حين أن فهم اللغة الطبيعية لا يزال الفهم المستندات الطويلة تحديا مفتوحا، غالبا ما تحتوي هذه الوثائق على معلومات هيكلية يمكنها إبلاغ تصميم النماذج التي ترميزها.البرامج النصية للأفلام هي مثال لمثل هذه النصوص النيكلية منظم، يتم تجزئة البرامج النصية في مشاهد، والتي تتحلل في الحوار والمكونات الوصفية.في هذا العمل، نقترح بنية عصبية لتشفير هذا الهيكل، والذي ينفذ بقوة على مهام تصنيف العلامات متعددة الملصقات دون استخدام ميزات يدويا.نضيف طبقة من البصيرة عن طريق زيادة وحدة الترجمة ذات القدرة على الترجمة الترجمة الترجمة غير المنشطة، والتي يمكن استخدامها لاستخراج وتصور المسارات السردية.على الرغم من أن هذا العمل يتناول screenplays على وجه التحديد، فإننا نناقش كيف يمكن تعميم النهج الأساسي لمجموعة من الوثائق المهيكلة.
While natural language understanding of long-form documents remains an open challenge, such documents often contain structural information that can inform the design of models encoding them. Movie scripts are an example of such richly structured text -- scripts are segmented into scenes, which decompose into dialogue and descriptive components. In this work, we propose a neural architecture to encode this structure, which performs robustly on two multi-label tag classification tasks without using handcrafted features. We add a layer of insight by augmenting the encoder with an unsupervised interpretability' module, which can be used to extract and visualize narrative trajectories. Though this work specifically tackles screenplays, we discuss how the underlying approach can be generalized to a range of structured documents.
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