RAPL: A Relation-Aware Prototype Learning Approach for Few-Shot
Document-Level Relation Extraction

RAPL: A Relation-Aware Prototype Learning Approach for Few-Shot Document-Level Relation Extraction

24 October 2023

Aiwei Liu

Lijie Wen

Papers citing "RAPL: A Relation-Aware Prototype Learning Approach for Few-Shot Document-Level Relation Extraction"

7 / 7 papers shown

Title
Graph-Augmented Relation Extraction Model with LLMs-Generated Support Document Vicky Dong Hao Yu Yao Chen 29 0 0 30 Oct 2024
TPN: Transferable Proto-Learning Network towards Few-shot Document-Level Relation Extraction Yu Zhang Zhao Kang ViT 39 0 0 01 Oct 2024
On the Robustness of Document-Level Relation Extraction Models to Entity Name Variations Shiao Meng Xuming Hu Aiwei Liu Fukun Ma Yawen Yang Shuang Li Lijie Wen 44 0 0 11 Jun 2024
Efficient Information Extraction in Few-Shot Relation Classification through Contrastive Representation Learning Philipp Borchert Jochen De Weerdt Marie-Francine Moens 27 1 0 25 Mar 2024
Robust Text Classification: Analyzing Prototype-Based Networks Zhivar Sourati D. Deshpande Filip Ilievski Kiril Gashteovski S. Saralajew OOD OffRL 34 2 0 11 Nov 2023
Large Language Model Is Not a Good Few-shot Information Extractor, but a Good Reranker for Hard Samples! Yubo Ma Yixin Cao YongChing Hong Aixin Sun RALM 80 135 0 15 Mar 2023
Gradient Imitation Reinforcement Learning for Low Resource Relation Extraction Xuming Hu Chenwei Zhang Yawen Yang Xiaohe Li Li Lin Lijie Wen Philip S. Yu 51 59 0 14 Sep 2021