Title: Protecting Your NLG Models with Semantic and Robust Watermarks. Abstract: Natural language generation (NLG) applications have gained great popularity due to the powerful deep learning techniques and large training corpus. The deployed NLG models may be stolen or used without authorization, while watermark has become a useful tool to protect Intellectual Property (IP). However, existing watermark technologies are easily detected or harmful for the applications. In this paper, we propose a semantic and robust watermarking scheme for NLG models that utilize pair-matched phrases as watermarks for IP protection. The watermarks give NLG models personal preference for some special phrase combinations. When the key phrase appears behinds a specific prefix phrase, the model would give the congenial predication for the key phrase. We use word tag n-gram to generate semantic watermark which is syntax correctly. For the key phrase's predication, we choose the original model's second predication, which makes nearly no harmfulness to the task and also undetectable. Extensive experimental results demonstrate the effectiveness, robustness, and undetectability of the proposed scheme.