75 citations · 90 across the 5 of their papers we have counts for
5 papers
Differentiable Prompt Makes Pre-trained Language Models Better Few-shot Learners
Ningyu Zhang, Luoqiu Li, Xiang Chen +5
Large-scale pre-trained language models have contributed significantly to natural language processing by demonstrating remarkable abilities as few-shot learners. However, their eff…
OntoED: Low-resource Event Detection with Ontology Embedding
Shumin Deng, Ningyu Zhang, Luoqiu Li +5
Event Detection (ED) aims to identify event trigger words from a given text and classify it into an event type. Most of current methods to ED rely heavily on training instances, an…
Text-guided Legal Knowledge Graph Reasoning
Luoqiu Li, Zhen Bi, Hongbin Ye +3
Recent years have witnessed the prosperity of legal artificial intelligence with the development of technologies. In this paper, we propose a novel legal application of legal provi…
Normal vs. Adversarial: Salience-based Analysis of Adversarial Samples for Relation Extraction
Luoqiu Li, Xiang Chen, Zhen Bi +6
Recent neural-based relation extraction approaches, though achieving promising improvement on benchmark datasets, have reported their vulnerability towards adversarial attacks. Thu…
On Robustness and Bias Analysis of BERT-based Relation Extraction
Luoqiu Li, Xiang Chen, Hongbin Ye +4
Fine-tuning pre-trained models have achieved impressive performance on standard natural language processing benchmarks. However, the resultant model generalizability remains poorly…