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Register a new userImproving Neural RST Parsing Model with Silver Agreement Subtrees
تحسين نموذج التحليل العصبي RST مع اتفاقية الفضة
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تستند معظم طرق تحليل البنية الخطابية السابقة (RST) إلى التعلم الخاضع للإشراف مثل الشبكات العصبية، والتي تتطلب وجعة مشروح من الحجم والجودة الكافية. ومع ذلك، فإن Treebank Treebank RST RST (RST-DT)، والجورباس القياسي للحل الصادر باللغة الإنجليزية، وهو صغير بسبب التعليق التوضيحي بشكل مكلف للأشجار الأولى. عدم وجود بيانات تدريبية كبيرة مشروحة تسبب أداء ضعيف خاصة في العلامات المتعلقة بالعلامات. لذلك، نقترح طريقة لتحسين نماذج التحليل العصبي RST من خلال استغلال البيانات الفضية، أي البيانات المشروحة تلقائيا. نقوم بإنشاء بيانات فضية واسعة النطاق من Corpus غير المستمر باستخدام محلل دائري للحكومة الأولى. للحصول على بيانات فضية عالية الجودة، نستخلص من الاتفاقية من الأشجار الأولى للوثائق التي تم بناؤها باستخدام المحللين RST. بعد ذلك، قم بتدريب المحلل الوراثي العصبي مع البيانات الفضية التي تم الحصول عليها وضبطها بشكل جيد على RST-DT. تظهر النتائج التجريبية أن طريقتنا حققت أفضل درجات Micro-F1 للأرضيات القومية والعلاقة عند 75.0 و 63.2 على التوالي. علاوة على ذلك، حصلنا على مكاسب ملحوظة في درجة العلاقة، 3.0 نقطة، ضد المحللين السابقين من الحديثة.
Most of the previous Rhetorical Structure Theory (RST) parsing methods are based on supervised learning such as neural networks, that require an annotated corpus of sufficient size and quality. However, the RST Discourse Treebank (RST-DT), the benchmark corpus for RST parsing in English, is small due to the costly annotation of RST trees. The lack of large annotated training data causes poor performance especially in relation labeling. Therefore, we propose a method for improving neural RST parsing models by exploiting silver data, i.e., automatically annotated data. We create large-scale silver data from an unlabeled corpus by using a state-of-the-art RST parser. To obtain high-quality silver data, we extract agreement subtrees from RST trees for documents built using the RST parsers. We then pre-train a neural RST parser with the obtained silver data and fine-tune it on the RST-DT. Experimental results show that our method achieved the best micro-F1 scores for Nuclearity and Relation at 75.0 and 63.2, respectively. Furthermore, we obtained a remarkable gain in the Relation score, 3.0 points, against the previous state-of-the-art parser.
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