A Robust Adversarial Training Approach to Machine Reading Comprehension

時間 2020-12-30

標籤 NLP MRC 简体版

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A Robust Adversarial Training Approach to Machine Reading Comprehension 2020 AAAI 百度，北大，廈大動機：同樣是針對robustness，one of the most promising ways is to augment the training dataset Since the types of adve

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1. 論文《Adversarial Reading Networks For Machine Comprehension》
2. 論文筆記（5.12，文本抗擊，sentence）--Robust Machine Comprehension Models via Adversarial Training
3. 論文筆記--From Answer Extraction to Answer Generation for Machine Reading Comprehension (S-Net)
4. 論文閱讀——Robust Unsupervised Neural Machine Translation with Adversarial Training
5. 【論文筆記03】ReasoNet: Learning to Stop Reading in Machine Comprehension
6. 論文閱讀|Cross-Lingual Machine Reading Comprehension
7. 【論文翻譯+筆記】Neural Machine Reading Comprehension: Methods and Trends
8. A Thorough Examination of CNN/Daily Mail Reading Comprehension
9. ReasoNet: Learning to Stop Reading in Machine Comprehension讀書筆記
10. 【論文筆記】Improving Machine Reading Comprehension with General Reading Strategies（2019，NAACL）
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1. 論文《Adversarial Reading Networks For Machine Comprehension》
2. 論文筆記（5.12，文本抗擊，sentence）--Robust Machine Comprehension Models via Adversarial Training
3. 論文筆記--From Answer Extraction to Answer Generation for Machine Reading Comprehension (S-Net)
4. 論文閱讀——Robust Unsupervised Neural Machine Translation with Adversarial Training
5. 【論文筆記03】ReasoNet: Learning to Stop Reading in Machine Comprehension
6. 論文閱讀|Cross-Lingual Machine Reading Comprehension
7. 【論文翻譯+筆記】Neural Machine Reading Comprehension: Methods and Trends
8. A Thorough Examination of CNN/Daily Mail Reading Comprehension
9. ReasoNet: Learning to Stop Reading in Machine Comprehension讀書筆記
10. 【論文筆記】Improving Machine Reading Comprehension with General Reading Strategies（2019，NAACL）

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