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

arXiv: https://arxiv.org/abs/1609.01462 · HTML: https://mlsanity.com/paper/1609.01462/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Sep 2016.

## Built on by (most cited first, 19 of 19)

- [A Survey on Spoken Language Understanding: Recent Advances and New Frontiers](https://mlsanity.com/paper/2103.03095/) (2021)
- [GL-CLeF: A Global-Local Contrastive Learning Framework for Cross-lingual Spoken Language Understanding](https://mlsanity.com/paper/2204.08325/) (2022)
- [Towards Joint Intent Detection and Slot Filling via Higher-order Attention](https://mlsanity.com/paper/2109.08890/) (2021)
- [PSSAT: A Perturbed Semantic Structure Awareness Transferring Method for Perturbation-Robust Slot Filling](https://mlsanity.com/paper/2208.11508/) (2022)
- [A Streaming End-to-End Framework For Spoken Language Understanding](https://mlsanity.com/paper/2105.10042/) (2021)
- [Sequential End-to-End Intent and Slot Label Classification and Localization](https://mlsanity.com/paper/2106.04660/) (2021)
- [ASR Adaptation for E-commerce Chatbots using Cross-Utterance Context and Multi-Task Language Modeling](https://mlsanity.com/paper/2106.09532/) (2021)
- [Towards Robust and Generalizable Training: An Empirical Study of Noisy Slot Filling for Input Perturbations](https://mlsanity.com/paper/2310.03518/) (2023)
- [Speech Understanding on Tiny Devices with A Learning Cache](https://mlsanity.com/paper/2311.18188/) (2023)
- [CroPrompt: Cross-task Interactive Prompting for Zero-shot Spoken Language Understanding](https://mlsanity.com/paper/2406.10505/) (2024)
- [Joint Intent Detection and Slot Filling with Wheel-Graph Attention Networks](https://mlsanity.com/paper/2102.04610/) (2021)
- [Zero-Shot Learning for Joint Intent and Slot Labeling](https://mlsanity.com/paper/2212.07922/) (2022)
- [Noise-BERT: A Unified Perturbation-Robust Framework with Noise Alignment Pre-training for Noisy Slot Filling Task](https://mlsanity.com/paper/2402.14494/) (2024)
- [A survey of joint intent detection and slot-filling models in natural language understanding](https://mlsanity.com/paper/2101.08091/) (2021)
- [Confusion2vec 2.0: Enriching Ambiguous Spoken Language Representations with Subwords](https://mlsanity.com/paper/2102.02270/) (2021)
- [Improving End-to-End Models for Set Prediction in Spoken Language Understanding](https://mlsanity.com/paper/2201.12105/) (2022)
- [Modeling Output-Level Task Relatedness in Multi-Task Learning with Feedback Mechanism](https://mlsanity.com/paper/2404.00885/) (2024)
- [HC2L: Hybrid and Cooperative Contrastive Learning for Cross-lingual Spoken Language Understanding](https://mlsanity.com/paper/2405.06204/) (2024)
- [A Generative Model for Joint Multiple Intent Detection and Slot Filling](https://mlsanity.com/paper/2602.08322/) (2026)

## Abstract (arXiv)

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.