| Challenge: | Structured dropout approaches have been investigated to regularize the multi-head attention mechanism in Transformers. |
| Approach: | They propose a new regularization scheme based on token-level rather than structure-level to reduce overfitting by manipulating the connections between tokens in the multi-head attention via masking. |
| Outcome: | The proposed regularization scheme outperforms attention dropout and DropHead on 18 datasets and can establish a new record on the data-to-text benchmark Rotowire (18.93 BLEU). |
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| Challenge: | DropHead is a structured dropout method for regularizing multi-head attention . DropHed drops entire attention heads during training to prevent overfitting . |
| Approach: | They propose a structured dropout method specifically designed for regularizing multi-head attention mechanism . DropHead drops entire attention heads during training to prevent overfitting . |
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Incorporating Residual and Normalization Layers into Analysis of Masked Language Models (2021.emnlp-main)
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| Challenge: | Transformer architecture is composed of multi-head attention, which has been extensively analyzed. |
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Syntax-Based Attention Masking for Neural Machine Translation (2021.naacl-srw)
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| Challenge: | Pre-trained transformers suffer from a computationally expensive self-attention mechanism that interacts with all tokens, including those unfavorable to classification performance. |
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Adaptive Transformers for Learning Multimodal Representations (2020.acl-srw)
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| Challenge: | Existing approaches for learning visiolinguistic representations with transformers are over-parametrized and require extensive training. |
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Guiding Attention for Self-Supervised Learning with Transformers (2020.findings-emnlp)
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Token Dropping for Efficient BERT Pretraining (2022.acl-long)
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UniDrop: A Simple yet Effective Technique to Improve Transformer without Extra Cost (2021.naacl-main)
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| Challenge: | Existing approaches to improve the performance of natural language processing models are over-parameterized and overfitted. |
| Approach: | They propose an approach to integrate dropout techniques into the training of Transformer models. |
| Outcome: | The proposed approach can achieve 1.5 BLEU improvement on IWSLT14 translation tasks and better accuracy for the classification even using strong pre-trained RoBERTa as backbone. |