106 citations · 209 across the 9 of their papers we have counts for
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cs.CL2020
Better Robustness by More Coverage: Adversarial Training with Mixup Augmentation for Robust Fine-tuning
Chenglei Si, Zhengyan Zhang, Fanchao Qi +4
Pretrained language models (PLMs) perform poorly under adversarial attacks. To improve the adversarial robustness, adversarial data augmentation (ADA) has been widely adopted to co…
cs.CL2020
CharBERT: Character-aware Pre-trained Language Model
Wentao Ma, Yiming Cui, Chenglei Si +3
Most pre-trained language models (PLMs) construct word representations at subword level with Byte-Pair Encoding (BPE) or its variations, by which OOV (out-of-vocab) words are almos…
cs.CL2020
Benchmarking Robustness of Machine Reading Comprehension Models
Chenglei Si, Ziqing Yang, Yiming Cui +3
Machine Reading Comprehension (MRC) is an important testbed for evaluating models' natural language understanding (NLU) ability. There has been rapid progress in this area, with ne…