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Register a new userStructure-aware Fine-tuning of Sequence-to-sequence Transformers for Transition-based AMR Parsing
الهيكل - يدرك ضبط محولات تسلسل إلى تسلسل لتحليل عمرو القائمة على الانتقال
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أدت مؤخرا مؤخرا الرسوم البيانية تم التنبؤ بمعنى التجريدي المعني (AMR) باستخدام نماذج محولات تسلسل التسلسل المدربة مسبقا إلى تحسينات كبيرة على معايير تحليل AMR. هذه المحللون بسيطة وتجنب النمذجة الصريحة للهيكل ولكن تفتقر إلى خصائص مرغوبة مثل ضمانات الرسوم البيانية بشكل جيد أو محاذاة الرسم الرسم البياني المدمج. في هذا العمل، نستكشف دمج نماذج لغة التسلسل العامة المدربة مسبقا ونهج بناء على الهيكل. نغادر من نظام انتقال مقره مؤشر واقتراح مجموعة انتقالية مبسطة، مصممة لتحسين استغلال نماذج اللغة المدربة مسبقا للضبط الناعم الهيكل. نحن نستكشف أيضا نمذجة ولاية المحللين داخل بنية فك التشفير المدربة مسبقا واستراتيجيات المفردات المختلفة لنفس الغرض. نحن نقدم مقارنة مفصلة مع التقدم المحرز الأخير في تحليل عمرو وإظهار أن المحلل المحلل المقترح يحتفظ بالخصائص المرغوبة للمناهج السابقة القائمة على الانتقال، بينما تكون أكثر بساطة والوصول إلى حالة التحليل الجديدة للفن AMR 2.0، دون الحاجة إلى إعادة الرسم البياني الفصداء.
Predicting linearized Abstract Meaning Representation (AMR) graphs using pre-trained sequence-to-sequence Transformer models has recently led to large improvements on AMR parsing benchmarks. These parsers are simple and avoid explicit modeling of structure but lack desirable properties such as graph well-formedness guarantees or built-in graph-sentence alignments. In this work we explore the integration of general pre-trained sequence-to-sequence language models and a structure-aware transition-based approach. We depart from a pointer-based transition system and propose a simplified transition set, designed to better exploit pre-trained language models for structured fine-tuning. We also explore modeling the parser state within the pre-trained encoder-decoder architecture and different vocabulary strategies for the same purpose. We provide a detailed comparison with recent progress in AMR parsing and show that the proposed parser retains the desirable properties of previous transition-based approaches, while being simpler and reaching the new parsing state of the art for AMR 2.0, without the need for graph re-categorization.
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