Joint Online Spoken Language Understanding and Language Modeling with Recurrent Neural Networks
This work addresses the need for more efficient and accurate SLU in dialogue systems, particularly in noisy ASR settings, though it is incremental as it builds on existing RNN-based methods.
The paper tackles the problem of improving spoken language understanding (SLU) and language modeling in dialogue systems by proposing a recurrent neural network (RNN) model that jointly performs intent detection, slot filling, and language modeling, achieving an 11.8% relative reduction in perplexity and a 22.3% reduction in intent detection error rate on the ATIS dataset.
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.