Joint Intent Detection and Slot Filling with Convolutional Neural Networks

We describe a joint model for intent detection and slot filling based on convolutional neural networks (CNN). The proposed architecture can be perceived as a neural network (NN) version of the triangular CRF model (TriCRF), in which the intent label and the slot sequence are modeled jointly and their dependencies are exploited. Our slot filling component is a globally normalized CRF style model, as opposed to left-to-right models in recent NN based slot taggers. Its features are automatically extracted through CNN layers and shared by the intent model. We show that our slot model component generates state-of-the-art results, outperforming CRF significantly. Our joint model outperforms the standard TriCRF by 1% absolute for both intent and slot. On a number of other domains, our joint model achieves 0.7 – 1%, and 0.9 – 2.1% absolute gains over the independent modeling approach for intent and slot respectively.