118 citations · 443 across the 21 of their papers we have counts for
12 papers · 1 filter
Learning from Language Description: Low-shot Named Entity Recognition via Decomposed Framework
Yaqing Wang, Haoda Chu, Chao Zhang +1
In this work, we study the problem of named entity recognition (NER) in a low resource scenario, focusing on few-shot and zero-shot settings. Built upon large-scale pre-trained lan…
Calibrated Language Model Fine-Tuning for In- and Out-of-Distribution Data
Lingkai Kong, Haoming Jiang, Yuchen Zhuang +3
Fine-tuned pre-trained language models can suffer from severe miscalibration for both in-distribution and out-of-distribution (OOD) data due to over-parameterization. To mitigate t…
Text Classification Using Label Names Only: A Language Model Self-Training Approach
Yu Meng, Yunyi Zhang, Jiaxin Huang +4
Current text classification methods typically require a good number of human-labeled documents as training data, which can be costly and difficult to obtain in real applications. H…
Denoising Multi-Source Weak Supervision for Neural Text Classification
Wendi Ren, Yinghao Li, Hanting Su +3
We study the problem of learning neural text classifiers without using any labeled data, but only easy-to-provide rules as multiple weak supervision sources. This problem is challe…
SeqMix: Augmenting Active Sequence Labeling via Sequence Mixup
Rongzhi Zhang, Yue Yu, Chao Zhang
Active learning is an important technique for low-resource sequence labeling tasks. However, current active sequence labeling methods use the queried samples alone in each iteratio…
Fine-Tuning Pre-trained Language Model with Weak Supervision: A Contrastive-Regularized Self-Training Approach
Yue Yu, Simiao Zuo, Haoming Jiang +3
Fine-tuned pre-trained language models (LMs) have achieved enormous success in many natural language processing (NLP) tasks, but they still require excessive labeled data in the fi…