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Register a new userModeling Graph Structure via Relative Position for Text Generation from Knowledge Graphs
هيكل الرسم البياني النمذجة عن طريق الوضع النسبي لجنة النص من الرسوم البيانية المعرفة
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نحن نقدم Graformer، وهي عبارة عن بنية ترميز ترميز ترميز محول المبالية على أساس الرسوم البيانية إلى النص.مع انتباهنا عن الرسوم البيانية لروايتنا، يعتمد ترميز العقدة على جميع العقد في الرسم البياني للإدخال - ليس فقط الجيران المباشر - يسهل اكتشاف أنماط عالمية.نحن نمثل العلاقة بين العقدتين كطابع أقصر المسار بينهما.يتعلم Graformer الوزن هذه العلاقات العقدة العقدة بشكل مختلف عن رؤوس اهتمام مختلفة، وبالتالي تعلم وجهات نظر متصلة بشكل مختلف عن الرسم البياني للإدخال.نقوم بتقييم GRAFORMER على اثنين من المعايير الشهيرة في الرسم البياني إلى النص، وجدول الأعمال و Webnlg، حيث يحقق أداء قوي أثناء استخدام العديد من المعلمات أقل من الأساليب الأخرى.
We present Graformer, a novel Transformer-based encoder-decoder architecture for graph-to-text generation. With our novel graph self-attention, the encoding of a node relies on all nodes in the input graph - not only direct neighbors - facilitating the detection of global patterns. We represent the relation between two nodes as the length of the shortest path between them. Graformer learns to weight these node-node relations differently for different attention heads, thus virtually learning differently connected views of the input graph. We evaluate Graformer on two popular graph-to-text generation benchmarks, AGENDA and WebNLG, where it achieves strong performance while using many fewer parameters than other approaches.
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