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Register a new userRecursive Non-Autoregressive Graph-to-Graph Transformer for Dependency Parsing with Iterative Refinement
محول الرسم البياني غير الرسمي غير الرسمي غير التلقائي لتخليص التبعية مع تحسين التكرار
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نقترح هندسة محول الرسم البياني المتكرر للرسوم البيانية التلقائي (Rngtr) من أجل تحسين الرسوم البيانية التعسفية من خلال التطبيق العسكري لمحول الرسم البياني غير التلقائي إلى الرسم البياني وتطبيقه على تحليل التبعية النحوية.نوضح قوة وفعالية Rngtr على العديد من شركات التبعية، باستخدام نموذج التقييم المدرب مسبقا مع بيرت.نقدم أيضا محولات محول النحوية (Sytr)، وهي محلل غير متكرر مشابهة لنموذج التقييم الخاص بنا.يمكن Rngtr تحسين دقة مجموعة متنوعة من المحللين الأوليين في 13 لغة من التبعيات الشاملة TreeBanks والإنجليزية والصينية Benn Treebanks، والجوربوس الألماني Conll2009، وحتى تحسين النتائج الجديدة على النتائج الجديدة التي حققتها Systr، بشكل كبيرتحسين أحدث حديثة لجميع الشركات التي تم اختبارها.
We propose the Recursive Non-autoregressive Graph-to-Graph Transformer architecture (RNGTr) for the iterative refinement of arbitrary graphs through the recursive application of a non-autoregressive Graph-to-Graph Transformer and apply it to syntactic dependency parsing. We demonstrate the power and effectiveness of RNGTr on several dependency corpora, using a refinement model pre-trained with BERT. We also introduce Syntactic Transformer (SynTr), a non-recursive parser similar to our refinement model. RNGTr can improve the accuracy of a variety of initial parsers on 13 languages from the Universal Dependencies Treebanks, English and Chinese Penn Treebanks, and the German CoNLL2009 corpus, even improving over the new state-of-the-art results achieved by SynTr, significantly improving the state-of-the-art for all corpora tested.
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