Edge-centric optimization: a novel strategy for minimizing information loss in graph-to-text generation
Abstract Given the remarkable text generation capabilities of pre-trained language models, impressive results have been realized in graph-to-text generation. However, while learning from knowledge graphs, these language models are unable to fully grasp the structural information of the graph, leadin...
Saved in:
Main Authors: | Zheng Yao, Jingyuan Li, Jianhe Cen, Shiqi Sun, Dahu Yin, Yuanzhuo Wang |
---|---|
Format: | Article |
Language: | English |
Published: |
Springer
2024-12-01
|
Series: | Complex & Intelligent Systems |
Subjects: | |
Online Access: | https://doi.org/10.1007/s40747-024-01690-y |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
CoGraphNet for enhanced text classification using word-sentence heterogeneous graph representations and improved interpretability
by: Pengyi Li, et al.
Published: (2025-01-01) -
A Few-Shot Knowledge Graph Completion Model With Neighbor Filter and Affine Attention
by: Hongfang Gong, et al.
Published: (2025-01-01) -
Edge coloring of small signed graphs
by: Robert Janczewski, et al.
Published: (2025-01-01) -
Towards leveraging explicit negative statements in knowledge graph embeddings
by: Rita T. Sousa, et al.
Published: (2025-01-01) -
Learner preferences prediction with mixture embedding of knowledge and behavior graph
by: Xiaoguang LI, et al.
Published: (2021-08-01)