Papers with BERT-base model
On Attention Redundancy: A Comprehensive Study (2021.naacl-main)
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| Challenge: | Attention redundancy has been observed among attention heads but has not been deeply studied in the literature. |
| Approach: | They propose a multi-layer multi-head self-attention mechanism which is widely applied in modern neural language models. |
| Outcome: | The proposed model is useful for interpretation and model compression. |
Posterior Differential Regularization with f-divergence for Improving Model Robustness (2021.naacl-main)
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| Challenge: | Recent studies show that pre-trained models suffer catastrophic degradation in out-of-domain generalization to datasets with domain shift or adversarial scenarios. |
| Approach: | They propose to regularize the posterior difference between clean and noisy inputs by using a Jacobian regularization framework and a virtual adversarial training framework. |
| Outcome: | The proposed framework can improve model robustness in fully supervised and semi-supervised settings. |
Compressing Pre-trained Language Models by Matrix Decomposition (2020.aacl-main)
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| Challenge: | Large pre-trained language models reach state-of-the-art results when fine-tuned individually; They also come with a significant memory and computational requirements, calling for methods to reduce model sizes (green AI). |
| Approach: | They propose a two-stage model-compression method to reduce a model’s inference time cost by decompressing the model into smaller matrices and performing feature distillation on the internal representation. |
| Outcome: | The proposed method reduces the number of parameters by 0.4x and increases inference speed by 1.45x while preserving the information contained within the model. |
Learning to Ask Unanswerable Questions for Machine Reading Comprehension (P19-1)
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| Challenge: | Existing models for extractive reading comprehension are not good at deciding whether no answer is presented in the context. |
| Approach: | They propose a data augmentation technique by automatically generating relevant unanswerable questions according to an answerable question paired with its corresponding paragraph that contains the answer. |
| Outcome: | The proposed model performs better on the SQuAD 2.0 dataset than the baseline model and the BERT-large model. |
R-AT: Regularized Adversarial Training for Natural Language Understanding (2022.findings-emnlp)
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| Challenge: | Currently, adversarial training is a popular and powerful regularization method in the natural language domain. |
| Approach: | They propose to regularize adversarial training via dropout by perturbing word embeddings . they find that R-AT can improve many models by reducing adversariality . |
| Outcome: | The proposed method can reduce the inconsistency between training and testing of models with dropout. |
PruMUX: Augmenting Data Multiplexing with Model Compression (2023.findings-acl)
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| Challenge: | Prior work has investigated methods like model pruning, knowledge distillation, and data multiplexing to increase model throughput without sacrificing accuracy. |
| Approach: | They propose to combine structured pruning and data multiplexing methods to increase model throughput without sacrificing accuracy. |
| Outcome: | The proposed method achieves 7.5-29.5X throughput improvement over a BERT-base model with accuracy threshold from 80% to 74%. |