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Register a new userNeural Language Modeling for Contextualized Temporal Graph Generation
نمذجة اللغة العصبية لتوليد الرسم البياني الفني
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تقدم هذه الورقة أول دراسة حول استخدام نماذج اللغة المدربة مسبقا على نطاق واسع للجيل الآلي من الرسم البياني الصخم على مستوى الحدث للحصول على مستند. على الرغم من النجاح الهائل لأساليب ما قبل التدريب العصبي في مهام NLP، لم يتم استكشاف إمكاناتها للمنطق الزمني على الرسوم البيانية في الأحداث بما فيه الكفاية. جزء من السبب هو صعوبة في الحصول على شركة تدريبية كبيرة مع أحداث مشروح بين الإنسان والروابط الزمنية. نحن نتطلع إلى هذا التحدي باستخدام أدوات IE / NLP الحالية لتوليد كمية كبيرة تلقائيا (89،000) من أزواج المستند المستندات المنتجة للنظام، واقتراح صياغة رواية لمشكلة جيل الرسم البياني للسياق كقوة تعيين تسلسل إلى تسلسل. تمكننا هذه الاستراتيجيات من الاستفادة من النماذج اللغوية التي تم تدريبها مسبقا على بيانات التدريب التي يسببها النظام المهمة لتوليد الرسم البياني. تظهر تجاربنا أن نهجنا فعال للغاية في توليد رسوم بيانية صحيحة هيكليا وذات رأسيا. علاوة على ذلك، يوضح التقييم على Corpus تحديا يدويا عن طريق اليد، أن أسلوبنا تتفوق على أقرب طريقة موجودة من خلال هامش كبير على عدة مقاييس. نعرض أيضا تطبيقا نهرما لنهجنا من خلال تكييفه للإجابة على الأسئلة الزمنية المفتوحة في إعداد فهم القراءة.
This paper presents the first study on using large-scale pre-trained language models for automated generation of an event-level temporal graph for a document. Despite the huge success of neural pre-training methods in NLP tasks, its potential for temporal reasoning over event graphs has not been sufficiently explored. Part of the reason is the difficulty in obtaining large training corpora with human-annotated events and temporal links. We address this challenge by using existing IE/NLP tools to automatically generate a large quantity (89,000) of system-produced document-graph pairs, and propose a novel formulation of the contextualized graph generation problem as a sequence-to-sequence mapping task. These strategies enable us to leverage and fine-tune pre-trained language models on the system-induced training data for the graph generation task. Our experiments show that our approach is highly effective in generating structurally and semantically valid graphs. Further, evaluation on a challenging hand-labeled, out-of-domain corpus shows that our method outperforms the closest existing method by a large margin on several metrics. We also show a downstream application of our approach by adapting it to answer open-ended temporal questions in a reading comprehension setting.
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