  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. On
language modeling task, our joint model achieves 11.8% relative reduction on
perplexity comparing to the independent training language model. On SLU tasks,
our joint model outperforms the independent task training model by 22.3% on
intent detection error rate, with slight degradation on slot filling F1 score.
The joint model also shows advantageous performance in the realistic ASR
settings with noisy speech input.
