While sequence-to-sequence (seq2seq) models on semantic parsing have demonstrated significant performance, the need for large amounts of labeled data still hinders the application of this technology to resource-poor domains. In this work, we work on alleviating data scarcity in semantic parsing. We propose a semi-supervised semantic parsing methods by exploiting unlabeled natural utterances in a novel multi-task learning framework. Two strategies are proposed. The first one takes entity sequences as training targets to improve the representations of encoder and reduce entity-mistakes in prediction. The second one extends Mean Teacher to seq2seq model and generates more target-side data to improve the generalizability of decoder network. Experiments demonstrate that our proposed methods significantly outperform the supervised baseline and achieve more impressive improvement than previous methods.