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Register a new userExtracting Event Temporal Relations via Hyperbolic Geometry
استخراج العلاقات الزمنية للحدث عبر الهندسة القطعي
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الكشف عن الأحداث وتطورها عبر الزمن مهمة حاسمة في فهم اللغة الطبيعية. المناهج العصبية الأخيرة لحدث استخراج العلاقات الزمنية عادة الأحداث عادة إلى التشرد في مساحة Euclidean وتدريب مصنف للكشف عن العلاقات الزمنية بين أزواج الأحداث. ومع ذلك، لا يمكن للمشروع في الفضاء الإقليدية التقاط علاقات غير متماثلة أكثر ثراء مثل العلاقات الزمنية الحدث. وبالتالي اقترحنا تضمين الأحداث في المساحات القطعي، والتي هي موجهة نحو جوهرها في نمذجة الهياكل الهرمية. نقدم نهجين لتشفير الأحداث وعلاقاتهم الزمنية في المساحات القطعية. نهج واحد يرفع إلى تضمينات الزائفة لعلاقات الحدث المستخلصة مباشرة من خلال عمليات هندسية بسيطة. في المرتبة الثانية، ابتعدنا عن وضع بنية نهاية إلى نهاية مؤلفة من الوحدات العصبية الزمنية المصممة لمهمة استخراج العلاقة الزمنية. أظهرت تقييمات تجريبية شاملة عن مجموعات البيانات المستخدمة على نطاق واسع فوائد إعادة النظر في المهام على مساحة هندسية مختلفة، مما أدى إلى أداء حديثة في العديد من المقاييس القياسية. أخيرا، أبرزت دراسة الاجتثاث والعديد من التحليلات النوعية دلالات الأحداث الغنية المشفرة ضمنيا في المساحات الزائفة.
Detecting events and their evolution through time is a crucial task in natural language understanding. Recent neural approaches to event temporal relation extraction typically map events to embeddings in the Euclidean space and train a classifier to detect temporal relations between event pairs. However, embeddings in the Euclidean space cannot capture richer asymmetric relations such as event temporal relations. We thus propose to embed events into hyperbolic spaces, which are intrinsically oriented at modeling hierarchical structures. We introduce two approaches to encode events and their temporal relations in hyperbolic spaces. One approach leverages hyperbolic embeddings to directly infer event relations through simple geometrical operations. In the second one, we devise an end-to-end architecture composed of hyperbolic neural units tailored for the temporal relation extraction task. Thorough experimental assessments on widely used datasets have shown the benefits of revisiting the tasks on a different geometrical space, resulting in state-of-the-art performance on several standard metrics. Finally, the ablation study and several qualitative analyses highlighted the rich event semantics implicitly encoded into hyperbolic spaces.
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