29 citations · 43 across the 8 of their papers we have counts for
5 papers · 1 filter
No Parameters Left Behind: Sensitivity Guided Adaptive Learning Rate for Training Large Transformer Models
Chen Liang, Haoming Jiang, Simiao Zuo +5
Recent research has shown the existence of significant redundancy in large Transformer models. One can prune the redundant parameters without significantly sacrificing the generali…
Self-Training with Differentiable Teacher
Simiao Zuo, Yue Yu, Chen Liang +5
Self-training achieves enormous success in various semi-supervised and weakly-supervised learning tasks. The method can be interpreted as a teacher-student framework, where the tea…
ARCH: Efficient Adversarial Regularized Training with Caching
Simiao Zuo, Chen Liang, Haoming Jiang +5
Adversarial regularization can improve model generalization in many natural language processing tasks. However, conventional approaches are computationally expensive since they nee…
Named Entity Recognition with Small Strongly Labeled and Large Weakly Labeled Data
Haoming Jiang, Danqing Zhang, Tianyu Cao +2
Weak supervision has shown promising results in many natural language processing tasks, such as Named Entity Recognition (NER). Existing work mainly focuses on learning deep NER mo…
Token-wise Curriculum Learning for Neural Machine Translation
Chen Liang, Haoming Jiang, Xiaodong Liu +4
Existing curriculum learning approaches to Neural Machine Translation (NMT) require sampling sufficient amounts of "easy" samples from training data at the early training stage. Th…