In this paper, we present our novel approach to the 6th Dialogue State Tracking Challenge (DSTC6) track for end-to-end goal-oriented dialogue, in which the goal is to select the best system response from among a list of candidates in a restaurant booking conversation. Our model uses a convolutional neural network (CNN) for semantic tagging of each utterance in the dialogue history to update the dialogue state, and another CNN for predicting the best system action template. Our model is competitive with the top two submissions to the challenge, achieving 100% precision on subtasks 1 and 2 with a CNN rather than an LSTM for action selection, and a CNN for slot-value tagging, instead of an LSTM or CRF.