Actor-Critic Network for Q&A in an Adversarial Environment
This work addresses robustness in Q&A systems for NLP applications, but it appears incremental as it integrates existing ideas.
The paper tackled the problem of improving robustness in Q&A NLP models against adversarial attacks by combining adversarial data generation with architectural modifications, showing promising results on the Adversarial SQuAD 'Add One Sent' dataset.
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.