Existing sarcasm detection approaches suffer from lack of sufficient training data. To address this problem, the authors propose an adversarial learning framework built on convolutional neural network (CNN) and attention mechanism, which is trained from limited amounts of labeled data. Two complementary adversarial learning approaches are investigated. First, by training with generated adversarial examples, the authors attempt to enhance the robustness and generalization ability of the classifier. Then, a domain transfer based adversarial learning approach is proposed to leverage cross-domain sarcasm data for improving the performance of sarcasm detection in the target domain. Experimental results on three sarcasm datasets show that both adversarial learning approaches proposed improve the performance of sarcasm detection, but the domain transfer based approach achieves higher performance. Combining the two proposed approaches further improves the performance of sarcasm detection.