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Register a new userEvaluating the Impact of a Hierarchical Discourse Representation on Entity Coreference Resolution Performance
تقييم تأثير تمثيل الخطاب التسلسل الهرمي حول أداء دقة كائن كوراسة
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يعمل العمل الحديث على قرار كائن كائن (CR) على اتجاه الاتجاهات الحالية في التعلم العميق المطبق على المدينات والميزات ذات الصلة بسيطة نسبيا.لا تستخدم نماذج Sota تمثيلات هرمية بنية الخطاب.في هذا العمل، نستفيد تلقائيا التي تم بناؤها تلقائيا تحليل الأشجار في نهج عصبي وإظهار تحسن كبير في مجموعات عمليتين من كائن كوريا القياسي.نستكشف كيف يختلف التأثير اعتمادا على نوع الإشارة.
Recent work on entity coreference resolution (CR) follows current trends in Deep Learning applied to embeddings and relatively simple task-related features. SOTA models do not make use of hierarchical representations of discourse structure. In this work, we leverage automatically constructed discourse parse trees within a neural approach and demonstrate a significant improvement on two benchmark entity coreference-resolution datasets. We explore how the impact varies depending upon the type of mention.
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We present an end-to-end neural approach to generate English sentences from formal meaning representations, Discourse Representation Structures (DRSs). We use a rather standard bi-LSTM sequence-to-sequence model, work with a linearized DRS input repr
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