activity
20172022
most citedS-Net: From Answer Extraction to Answer Generation for Machine Reading Comprehension

47 citations · 95 across the 13 of their papers we have counts for

collaborators

15 papers

cs.CL20222 cited

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…

cs.AI20222 cited

Parameter-Efficient Sparsity for Large Language Models Fine-Tuning

Yuchao Li, Fuli Luo, Chuanqi Tan +4

With the dramatically increased number of parameters in language models, sparsity methods have received ever-increasing research focus to compress and accelerate the models. While…

cs.CL20223 cited

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…

cs.CL2022

Code Synonyms Do Matter: Multiple Synonyms Matching Network for Automatic ICD Coding

Zheng Yuan, Chuanqi Tan, Songfang Huang

Automatic ICD coding is defined as assigning disease codes to electronic medical records (EMRs). Existing methods usually apply label attention with code representations to match r…

cs.CL20214 cited

Raise a Child in Large Language Model: Towards Effective and Generalizable Fine-tuning

Runxin Xu, Fuli Luo, Zhiyuan Zhang +4

Recent pretrained language models extend from millions to billions of parameters. Thus the need to fine-tune an extremely large pretrained model with a limited training corpus aris…

cs.CV20211 cited

Meta Gradient Adversarial Attack

Zheng Yuan, Jie Zhang, Yunpei Jia +3

In recent years, research on adversarial attacks has become a hot spot. Although current literature on the transfer-based adversarial attack has achieved promising results for impr…