189 citations · 872 across the 53 of their papers we have counts for
9 papers · 2 filters
Model Uncertainty-Aware Knowledge Amalgamation for Pre-Trained Language Models
Lei Li, Yankai Lin, Xuancheng Ren +4
As many fine-tuned pre-trained language models~(PLMs) with promising performance are generously released, investigating better ways to reuse these models is vital as it can greatly…
RAP: Robustness-Aware Perturbations for Defending against Backdoor Attacks on NLP Models
Wenkai Yang, Yankai Lin, Peng Li +2
Backdoor attacks, which maliciously control a well-trained model's outputs of the instances with specific triggers, are recently shown to be serious threats to the safety of reusin…
Dynamic Knowledge Distillation for Pre-trained Language Models
Lei Li, Yankai Lin, Shuhuai Ren +3
Knowledge distillation~(KD) has been proved effective for compressing large-scale pre-trained language models. However, existing methods conduct KD statically, e.g., the student mo…
Text AutoAugment: Learning Compositional Augmentation Policy for Text Classification
Shuhuai Ren, Jinchao Zhang, Lei Li +2
Data augmentation aims to enrich training samples for alleviating the overfitting issue in low-resource or class-imbalanced situations. Traditional methods first devise task-specif…
O2NA: An Object-Oriented Non-Autoregressive Approach for Controllable Video Captioning
Fenglin Liu, Xuancheng Ren, Xian Wu +4
Video captioning combines video understanding and language generation. Different from image captioning that describes a static image with details of almost every object, video capt…
Learning Relation Alignment for Calibrated Cross-modal Retrieval
Shuhuai Ren, Junyang Lin, Guangxiang Zhao +5
Despite the achievements of large-scale multimodal pre-training approaches, cross-modal retrieval, e.g., image-text retrieval, remains a challenging task. To bridge the semantic ga…