Label-Enhanced Reading Comprehension Model

In the existing extractive reading comprehension models,only the boundary of answers is utilized as the supervision signal and the labeling processed by human is ignored.Consequently,learned models are prone to learn the superficial features and the generalization performance is degraded.In this paper,a label-enhanced reading comprehension model is proposed to imitate human activity.The answer-bearing sentence,the content and the boundary of the answer are learned simultaneously.The answer-bearing sentence and the content of the answer can be derived from the boundary of the answer and these three types of labels are regarded as supervision signals.The model is trained by multitask learning.During prediction,the probabilities from three predictions are merged to determine the answer,and thus the generalization performance is improved.Experiments on SQuAD dataset demonstrate the effectiveness of LE-Reader model.