Papers with Quasar-T
Beyond Reptile: Meta-Learned Dot-Product Maximization between Gradients for Improved Single-Task Regularization (2021.findings-emnlp)
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| Challenge: | Existing approaches to improve generalization of neural models use a small component of the gradient for maximizing dot-product between batches. |
| Approach: | They propose to use a finite differences first-order algorithm to calculate a gradient from dot-product of gradients and regularize it. |
| Outcome: | The proposed method outperforms previous approaches of Reptile and MAML when used as a regularization technique. |
BERTAC: Enhancing Transformer-based Language Models with Adversarially Pretrained Convolutional Neural Networks (2021.acl-long)
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| Challenge: | Existing models of NLP are fading away, but new ones are needed to maintain their dominance. |
| Approach: | They propose a method to pretrain a CNN using Wikipedia data and integrate it with standard TLMs. |
| Outcome: | The proposed method outperforms the original ALBERT on GLUE tasks and achieves similar performance to SOTA on open-domain QA tasks. |
Token-level Dynamic Self-Attention Network for Multi-Passage Reading Comprehension (P19-1)
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| Challenge: | Multi-passage reading comprehension requires the ability to combine cross-passages information and reason over multiple passages to infer the answer. |
| Approach: | They propose a Dynamic Self-attention Network (DynSAN) which processes cross-passage information at token-level and meanwhile avoids substantial computational costs. |
| Outcome: | The proposed model achieves state-of-the-art performance on the SearchQA, Quasar-T and WikiHop datasets and further ablation validates the effectiveness of its components. |
Ranking and Sampling in Open-Domain Question Answering (D19-1)
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| Challenge: | Existing approaches focus on positive paragraphs which contain the answer during training, making it disturbed by similar but irrelevant paragraphs during testing. |
| Approach: | They propose a ranking model leveraging the paragraph-question and the paragraph relevance to compute a confidence score for each paragraph. |
| Outcome: | Experiments on three datasets show that the proposed model advances the state of the art. |