Joint Online Spoken Language Understanding And Language Modeling With Recurrent Neural Networks

Abstract

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.

Stats

• citations58
• S2 citations—
• github stars0
• HF likes0
• heat score13.28
• arxiv keyliu2016joint

Related papers

• A Novel Bi-directional Interrelated Model For Joint Intent Detection And Slot Filling (2019)12.33
• RNN Based Incremental Online Spoken Language Understanding (2019)0.00
• Speech To Semantics: Improve ASR And NLU Jointly Via All-neural Interfaces (2020)9.03
• Joint Automatic Speech Recognition And Structure Learning For Better Speech Understanding (2025)0.00
• Recent Advances In End-to-end Spoken Language Understanding (2019)8.09
• PIN: A Novel Parallel Interactive Network For Spoken Language Understanding (2020)8.09
• End-to-end Architectures For Asr-free Spoken Language Understanding (2019)8.60
• On Joint Training With Interfaces For Spoken Language Understanding (2021)7.16