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Register a new userStructural Realization with GGNNs
الإدراك الهيكلي مع GGNNS
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في هذه الورقة، نحدد مهمة مجردة تسمى الإدراك الهيكلي الذي يولد كلمات تعطى بادئة للكلمات وتمثيل جزئي لشجرة تحليل.نقدم أيضا طريقة لحل مثيلات هذه المهمة باستخدام شبكة عصبية Graph بوابات (GGNN).نحن نقيمها بتدابير الدقة القياسية، وكذلك فيما يتعلق بالحيرة، التي تعمل فيها مقارنة مع العمل السابق على نمذجة اللغة لتحديد المعلومات المضافة إلى مهمة اختيار معجمية من خلال وجود المعرفة النحوية.إن إضافة العقد الداخلية لتحليل الشجرة إلى النموذج العصبي هذا يجب أن تحسن النموذج، مع احترام كل من الدقة وإلى تدابير تقليدية مثل الحيرة، قد يبدو غير متأكدا، لكن المحاولات السابقة لم تلبي نجاحا تقريبا.لقد تعلمنا أيضا أن الروابط العرضية من خلال تحليل شجرة تسول في دقة النموذج في توليد الصفة والأجزاء الاسمية من الكلام.
In this paper, we define an abstract task called structural realization that generates words given a prefix of words and a partial representation of a parse tree. We also present a method for solving instances of this task using a Gated Graph Neural Network (GGNN). We evaluate it with standard accuracy measures, as well as with respect to perplexity, in which its comparison to previous work on language modelling serves to quantify the information added to a lexical selection task by the presence of syntactic knowledge. That the addition of parse-tree-internal nodes to this neural model should improve the model, with respect both to accuracy and to more conventional measures such as perplexity, may seem unsurprising, but previous attempts have not met with nearly as much success. We have also learned that transverse links through the parse tree compromise the model's accuracy at generating adjectival and nominal parts of speech.
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