554 citations · 1k across the 21 of their papers we have counts for
17 papers · 1 filter
SpanProto: A Two-stage Span-based Prototypical Network for Few-shot Named Entity Recognition
Jianing Wang, Chengcheng Han, Chengyu Wang +5
Few-shot Named Entity Recognition (NER) aims to identify named entities with very little annotated data. Previous methods solve this problem based on token-wise classification, whi…
Knowledge Prompting in Pre-trained Language Model for Natural Language Understanding
Jianing Wang, Wenkang Huang, Qiuhui Shi +4
Knowledge-enhanced Pre-trained Language Model (PLM) has recently received significant attention, which aims to incorporate factual knowledge into PLMs. However, most existing metho…
Towards Unified Prompt Tuning for Few-shot Text Classification
Jianing Wang, Chengyu Wang, Fuli Luo +6
Prompt-based fine-tuning has boosted the performance of Pre-trained Language Models (PLMs) on few-shot text classification by employing task-specific prompts. Yet, PLMs are unfamil…
KECP: Knowledge Enhanced Contrastive Prompting for Few-shot Extractive Question Answering
Jianing Wang, Chengyu Wang, Minghui Qiu +4
Extractive Question Answering (EQA) is one of the most important tasks in Machine Reading Comprehension (MRC), which can be solved by fine-tuning the span selecting heads of Pre-tr…
Making Pre-trained Language Models End-to-end Few-shot Learners with Contrastive Prompt Tuning
Ziyun Xu, Chengyu Wang, Minghui Qiu +4
Pre-trained Language Models (PLMs) have achieved remarkable performance for various language understanding tasks in IR systems, which require the fine-tuning process based on label…
HRKD: Hierarchical Relational Knowledge Distillation for Cross-domain Language Model Compression
Chenhe Dong, Yaliang Li, Ying Shen +1
On many natural language processing tasks, large pre-trained language models (PLMs) have shown overwhelming performances compared with traditional neural network methods. Neverthel…