论文标题
在对抗环境中用于问答环境的演员批评网络
Actor-Critic Network for Q&A in an Adversarial Environment
论文作者
论文摘要
在问答NLP空间中，已经进行了重大工作，以构建对对抗性攻击更强大的模型。重点的两个关键领域是生成对抗数据，以针对这些情况进行培训或修改现有的体系结构以建立鲁棒性。本文介绍了一种将这两个想法融合在一起的方法，以训练评论家模型，以在几乎增强的学习框架中使用。使用对抗小队“添加一个已发送”数据集，我们表明，这种方法有一些有希望的迹象可以防止对抗性攻击。
Significant work has been placed in the Q&A NLP space to build models that are more robust to adversarial attacks. Two key areas of focus are in generating adversarial data for the purposes of training against these situations or modifying existing architectures to build robustness within. This paper introduces an approach that joins these two ideas together to train a critic model for use in an almost reinforcement learning framework. Using the Adversarial SQuAD "Add One Sent" dataset we show that there are some promising signs for this method in protecting against Adversarial attacks.
