  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.
