51 citations · 118 across the 35 of their papers we have counts for
4 papers · 1 filter
Task-Adaptive Pre-Training for Boosting Learning With Noisy Labels: A Study on Text Classification for African Languages
Dawei Zhu, Michael A. Hedderich, Fangzhou Zhai +2
For high-resource languages like English, text classification is a well-studied task. The performance of modern NLP models easily achieves an accuracy of more than 90% in many stan…
Meta Self-Refinement for Robust Learning with Weak Supervision
Dawei Zhu, Xiaoyu Shen, Michael A. Hedderich +1
Training deep neural networks (DNNs) under weak supervision has attracted increasing research attention as it can significantly reduce the annotation cost. However, labels from wea…
MCSE: Multimodal Contrastive Learning of Sentence Embeddings
Miaoran Zhang, Marius Mosbach, David Ifeoluwa Adelani +2
Learning semantically meaningful sentence embeddings is an open problem in natural language processing. In this work, we propose a sentence embedding learning approach that exploit…
Is BERT Robust to Label Noise? A Study on Learning with Noisy Labels in Text Classification
Dawei Zhu, Michael A. Hedderich, Fangzhou Zhai +2
Incorrect labels in training data occur when human annotators make mistakes or when the data is generated via weak or distant supervision. It has been shown that complex noise-hand…