Abstract Identifying the unknown (novel) user intents that have never appeared in the training set is a challenging task in the dialogue system. In this paper, we present a two-stage method for detecting unknown intents. We use bidirec- tional long short-term memory (BiLSTM) net- work with the margin loss as the feature ex- tractor. With margin loss, we can learn dis- criminative deep features by forcing the net- work to maximize inter-class variance and to minimize intra-class variance. Then, we feed the feature vectorsto the density-based novelty detection algorithm, local outlier factor (LOF), to detect unknown intents. Experiments on two benchmark datasets show that our method can yield consistent improvements compared with the baseline methods.