In task-oriented dialogue systems, dialogue state tracking (DST) is responsible for estimating the current belief state of a dialogue. Recent research tends to utilize historical information to predict the states. However, most of them lack an efficient attention mechanism to comprehend the utterances well. Besides, existing methods usually ignore the relevance between the current utterances with the earlier ones, which determines whether or not the states change. In this paper, we propose an HDST-MRC (Hierarchical Dialogue State Tracking with Machine Reading Comprehension) model to tackle these issues. Within HSDT-MRC, we introduce bi-directional attention flow to extract a context span as the evidence of the ground truth and leverage another copy-augmented generator to predict the states. Experimental results on MultiWoz 2.0 and MultiWoz 2.1 demonstrate that our model achieves significant improvement compared with the baselines.