Model Agnostic Answer Reranking System for Adversarial Question Answering
This work addresses the problem of improving robustness against adversarial attacks for question answering models, which is relevant for developers and users of QA systems.
This paper introduces a model-agnostic answer reranking system for adversarial question answering. It improves performance over state-of-the-art defense techniques without requiring model retraining.
While numerous methods have been proposed as defenses against adversarial examples in question answering (QA), these techniques are often model specific, require retraining of the model, and give only marginal improvements in performance over vanilla models. In this work, we present a simple model-agnostic approach to this problem that can be applied directly to any QA model without any retraining. Our method employs an explicit answer candidate reranking mechanism that scores candidate answers on the basis of their content overlap with the question before making the final prediction. Combined with a strong base QAmodel, our method outperforms state-of-the-art defense techniques, calling into question how well these techniques are actually doing and strong these adversarial testbeds are.