10 citations · 12 across the 3 of their papers we have counts for
4 papers
Beyond Prompting: Making Pre-trained Language Models Better Zero-shot Learners by Clustering Representations
Yu Fei, Ping Nie, Zhao Meng +2
Recent work has demonstrated that pre-trained language models (PLMs) are zero-shot learners. However, most existing zero-shot methods involve heavy human engineering or complicated…
Anticipating the Unseen Discrepancy for Vision and Language Navigation
Yujie Lu, Huiliang Zhang, Ping Nie +4
Vision-Language Navigation requires the agent to follow natural language instructions to reach a specific target. The large discrepancy between seen and unseen environments makes i…
A Pairwise Probe for Understanding BERT Fine-Tuning on Machine Reading Comprehension
Jie Cai, Zhengzhou Zhu, Ping Nie +1
Pre-trained models have brought significant improvements to many NLP tasks and have been extensively analyzed. But little is known about the effect of fine-tuning on specific tasks…
DC-BERT: Decoupling Question and Document for Efficient Contextual Encoding
Yuyu Zhang, Ping Nie, Xiubo Geng +3
Recent studies on open-domain question answering have achieved prominent performance improvement using pre-trained language models such as BERT. State-of-the-art approaches typical…