70 citations · 439 across the 63 of their papers we have counts for
9 papers · 1 filter
OpenPrompt: An Open-source Framework for Prompt-learning
Ning Ding, Shengding Hu, Weilin Zhao +4
Prompt-learning has become a new paradigm in modern natural language processing, which directly adapts pre-trained language models (PLMs) to -style prediction, autoregressiv…
Exploring Universal Intrinsic Task Subspace via Prompt Tuning
Yujia Qin, Xiaozhi Wang, Yusheng Su +10
Why can pre-trained language models (PLMs) learn universal representations and effectively adapt to broad NLP tasks differing a lot superficially? In this work, we empirically find…
Prompt-Learning for Fine-Grained Entity Typing
Ning Ding, Yulin Chen, Xu Han +6
As an effective approach to tune pre-trained language models (PLMs) for specific tasks, prompt-learning has recently attracted much attention from researchers. By using \textit{clo…
Knowledgeable Prompt-tuning: Incorporating Knowledge into Prompt Verbalizer for Text Classification
Shengding Hu, Ning Ding, Huadong Wang +5
Tuning pre-trained language models (PLMs) with task-specific prompts has been a promising approach for text classification. Particularly, previous studies suggest that prompt-tunin…
CLINE: Contrastive Learning with Semantic Negative Examples for Natural Language Understanding
Dong Wang, Ning Ding, Piji Li +1
Despite pre-trained language models have proven useful for learning high-quality semantic representations, these models are still vulnerable to simple perturbations. Recent works a…
Pre-Trained Models: Past, Present and Future
Xu Han, Zhengyan Zhang, Ning Ding +21
Large-scale pre-trained models (PTMs) such as BERT and GPT have recently achieved great success and become a milestone in the field of artificial intelligence (AI). Owing to sophis…
PTR: Prompt Tuning with Rules for Text Classification
Xu Han, Weilin Zhao, Ning Ding +2
Fine-tuned pre-trained language models (PLMs) have achieved awesome performance on almost all NLP tasks. By using additional prompts to fine-tune PLMs, we can further stimulate the…
Few-NERD: A Few-Shot Named Entity Recognition Dataset
Ning Ding, Guangwei Xu, Yulin Chen +5
Recently, considerable literature has grown up around the theme of few-shot named entity recognition (NER), but little published benchmark data specifically focused on the practica…
Prototypical Representation Learning for Relation Extraction
Ning Ding, Xiaobin Wang, Yao Fu +7
Recognizing relations between entities is a pivotal task of relational learning. Learning relation representations from distantly-labeled datasets is difficult because of the abund…