Despite that current reading comprehension systems have achieved significant advancements, their promising performances are often obtained at the cost of making an ensemble of numerous models. Besides, existing approaches are also vulnerable to adversarial attacks. This paper tackles these problems by leveraging knowledge distillation, which aims to transfer knowledge from an ensemble model to a single model. We first demonstrate that vanilla knowledge distillation applied to answer span prediction is effective for reading comprehension systems. We then propose two novel approaches that not only penalize the prediction on confusing answers but also guide the training with alignment information distilled from the ensemble. Experiments show that our best student model has only a slight drop of 0.4% F1 on the SQuAD test set compared to the ensemble teacher, while running 12× faster during inference. It even outperforms the teacher on adversarial SQuAD datasets and NarrativeQA benchmark.
In the context of reading comprehension or span prediction, REF have very recently shown that we can distill knowledge from an ensemble of models into a single model.
Attention-Guided Answer Distillation for Machine Reading Comprehension
ArXiv
ArXiv
Computer Science