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Register a new userConstrained Multi-Task Learning for Event Coreference Resolution
تقييد التعلم متعدد المهام لدقة حل السلاسة
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نقترح النموذج الفوري الحدث العصبي الذي يتم فيه تدريب كائن تحرير الحدث بشكل مشترك مع خمس مهام: اكتشاف الزناد، كائن كور السلاح، تصميم المشمول، اكتشاف Realis، استخراج الوسيطة.لتوجيه تعلم هذا النموذج المعقد، فإننا ندمج قيود الاتساق عبر المهام في عملية التعلم كقيوب ناعمة من خلال تصميم عقوبة الوظائف.بالإضافة إلى ذلك، نقترح فكرة الرواية المتمثلة في عرض كائن العناية والحقوق الحدث بمهمة كائن تحرير واحدة، والتي نعتقد أنها خطوة نحو نموذج موحد لدق السلاح.ينتج النموذج الناتج نتائج أحدث النتائج في مجموعة بيانات QBP 2017 Aquerence.
We propose a neural event coreference model in which event coreference is jointly trained with five tasks: trigger detection, entity coreference, anaphoricity determination, realis detection, and argument extraction. To guide the learning of this complex model, we incorporate cross-task consistency constraints into the learning process as soft constraints via designing penalty functions. In addition, we propose the novel idea of viewing entity coreference and event coreference as a single coreference task, which we believe is a step towards a unified model of coreference resolution. The resulting model achieves state-of-the-art results on the KBP 2017 event coreference dataset.
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