158 citations · 322 across the 31 of their papers we have counts for
Showing 2021 · cs.CLShow all
2 papers · 2 filters
cs.CL2021
From Dense to Sparse: Contrastive Pruning for Better Pre-trained Language Model Compression
Runxin Xu, Fuli Luo, Chengyu Wang +4
Pre-trained Language Models (PLMs) have achieved great success in various Natural Language Processing (NLP) tasks under the pre-training and fine-tuning paradigm. With large quanti…
cs.CL2021★ 13 cited
DKPLM: Decomposable Knowledge-enhanced Pre-trained Language Model for Natural Language Understanding
Taolin Zhang, Chengyu Wang, Nan Hu +4
Knowledge-Enhanced Pre-trained Language Models (KEPLMs) are pre-trained models with relation triples injecting from knowledge graphs to improve language understanding abilities. To…