activity
20202026
most citedDifferentiable Prompt Makes Pre-trained Language Models Better Few-shot Learners

75 citations · 244 across the 21 of their papers we have counts for

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Showing 2022Show all

5 papers · 1 filter

cs.AI2022★ 1 cited

Tele-Knowledge Pre-training for Fault Analysis

Zhuo Chen, Wen Zhang, Yufeng Huang +14

In this work, we share our experience on tele-knowledge pre-training for fault analysis, a crucial task in telecommunication applications that requires a wide range of knowledge no…

q-bio.QM2022★ 2 cited

Multi-modal Protein Knowledge Graph Construction and Applications

Siyuan Cheng, Xiaozhuan Liang, Zhen Bi +2

Existing data-centric methods for protein science generally cannot sufficiently capture and leverage biology knowledge, which may be crucial for many protein tasks. To facilitate r…

cs.CL2022★ 57 cited

Relphormer: Relational Graph Transformer for Knowledge Graph Representations

Zhen Bi, Siyuan Cheng, Jing Chen +3

Transformers have achieved remarkable performance in widespread fields, including natural language processing, computer vision and graph mining. However, vanilla Transformer archit…

cs.CL2022★ 2 cited

Contrastive Demonstration Tuning for Pre-trained Language Models

Xiaozhuan Liang, Ningyu Zhang, Siyuan Cheng +3

Pretrained language models can be effectively stimulated by textual prompts or demonstrations, especially in low-data scenarios. Recent works have focused on automatically searchin…

q-bio.BM2022★ 50 cited

OntoProtein: Protein Pretraining With Gene Ontology Embedding

Ningyu Zhang, Zhen Bi, Xiaozhuan Liang +6

Self-supervised protein language models have proved their effectiveness in learning the proteins representations. With the increasing computational power, current protein language…