Machine learning algorithms are now common in the state-of-the-art spoken language understanding models. But to reach good performance they must be trained on a potentially large amount of data which are not available for a variety of tasks and languages of interest. In this work, we present a novel zero-shot learning method, based on word embeddings, allowing to derive a full semantic parser for spoken language understanding. No annotated in-context data are needed, the ontological description of the target domain and generic word embedding features (learned from freely available general domain data) suffice to derive the model. Two versions are studied with respect to how the model parameters and decoding step are handled , including an extension of the proposed approach in the context of conditional random fields. We show that this model, with very little supervision, can reach instantly performance comparable to those obtained by either state-of-the-art carefully handcrafted rule-based or trained statistical models for extraction of dialog acts on the Dialog State Tracking test datasets (DSTC2 and 3). Index Terms: spoken language understanding, word embedding , zero-shot learning, out-of-domain training data.