论文笔记（5.12，文本抗击，sentence）--Robust Machine Comprehension Models via Adversarial Training
QA数据集，提出了一种在ADDsent对抗生成算法上的改进算法–AddSentDiverse。同样是为了消除原先算法生成的数据中潜在的统计规律，让模型无法忽略干扰的影响。 对抗样本消统 Random Distractor Placement 不同于ADDsend将distractor放到context的文末，采用随机插入的方法可以保证在不改变原先数据的依赖分布的前提下，对模型产生干扰，让模型无法忽
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相关文章
- 1. 论文笔记（5.12，文本抗击，sentence）--Probing Neural Network Comprehension of Natural Language Arguments
- 2. A Robust Adversarial Training Approach to Machine Reading Comprehension
- 3. 论文《Adversarial Reading Networks For Machine Comprehension》
- 4. 论文阅读——Robust Unsupervised Neural Machine Translation with Adversarial Training
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- 6. AAAI2018-Long Text Generation via Adversarial Training with Leaked Information论文笔记
- 7. 论文阅读笔记《GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training》
- 8. VITAL: VIsual Tracking via Adversarial Learning论文笔记
- 9. 论文笔记（5.13，文本抗击，sentence）--Trick Me If You Can: Human-in-the-Loop Generation of Adversarial Examples
- 10. 论文笔记——Adversarial Patch（对抗补丁）