Joint Online Spoken Language Understanding and Language Modeling with Recurrent Neural Networks

Understand

Speaker intent detection and semantic slot filling are two critical tasks in spoken language understanding (SLU) for dialogue systems.

• In this paper, we describe a recurrent neural network (RNN) model that jointly performs intent detection, slot filling, and language modeling.
• The neural network model keeps updating the intent estimation as word in the transcribed utterance arrives and uses it as contextual features in the joint model.
• Evaluation of the language model and online SLU model is made on the ATIS benchmarking data set.