Improving Lexical Embeddings for Robust Question Answering
This work addresses robustness issues in question answering for AI systems, but it is incremental as it builds on existing embedding techniques.
The paper tackled the problem of limited robustness in question answering models by proposing a representation enhancement approach with semantic and contextual constraints, resulting in significant robustness improvements on four adversarial test sets.
Recent techniques in Question Answering (QA) have gained remarkable performance improvement with some QA models even surpassed human performance. However, the ability of these models in truly understanding the language still remains dubious and the models are revealing limitations when facing adversarial examples. To strengthen the robustness of QA models and their generalization ability, we propose a representation Enhancement via Semantic and Context constraints (ESC) approach to improve the robustness of lexical embeddings. Specifically, we insert perturbations with semantic constraints and train enhanced contextual representations via a context-constraint loss to better distinguish the context clues for the correct answer. Experimental results show that our approach gains significant robustness improvement on four adversarial test sets.