# OneNet: Joint Domain, Intent, Slot Prediction for Spoken Language Understanding

arXiv: https://arxiv.org/abs/1801.05149 · HTML: https://mlsanity.com/paper/1801.05149/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jan 2018.

## Built on by (most cited first, 13 of 13)

- [A Survey on Spoken Language Understanding: Recent Advances and New Frontiers](https://mlsanity.com/paper/2103.03095/) (2021)
- [Enhancing the Generalization for Intent Classification and Out-of-Domain Detection in SLU](https://mlsanity.com/paper/2106.14464/) (2021)
- [Task-Oriented Dialogue System as Natural Language Generation](https://mlsanity.com/paper/2108.13679/) (2021)
- [Automatic Intent-Slot Induction for Dialogue Systems](https://mlsanity.com/paper/2103.08886/) (2021)
- [Do as I mean, not as I say: Sequence Loss Training for Spoken Language Understanding](https://mlsanity.com/paper/2102.06750/) (2021)
- [Multimodal Audio-textual Architecture for Robust Spoken Language Understanding](https://mlsanity.com/paper/2306.06819/) (2023)
- [Real-time Caller Intent Detection In Human-Human Customer Support Spoken Conversations](https://mlsanity.com/paper/2208.06802/) (2022)
- [Out-of-Scope Domain and Intent Classification through Hierarchical Joint Modeling](https://mlsanity.com/paper/2104.14781/) (2021)
- [A Joint and Domain-Adaptive Approach to Spoken Language Understanding](https://mlsanity.com/paper/2107.11768/) (2021)
- [Tri-level Joint Natural Language Understanding for Multi-turn Conversational Datasets](https://mlsanity.com/paper/2305.17729/) (2023)
- [Compositional Generalization in Spoken Language Understanding](https://mlsanity.com/paper/2312.15815/) (2023)
- [A survey of joint intent detection and slot-filling models in natural language understanding](https://mlsanity.com/paper/2101.08091/) (2021)
- [DORIC : Domain Robust Fine-Tuning for Open Intent Clustering through Dependency Parsing](https://mlsanity.com/paper/2303.09827/) (2023)

## Abstract (arXiv)

In practice, most spoken language understanding systems process user input in a pipelined manner; first domain is predicted, then intent and semantic slots are inferred according to the semantic frames of the predicted domain. The pipeline approach, however, has some disadvantages: error propagation and lack of information sharing. To address these issues, we present a unified neural network that jointly performs domain, intent, and slot predictions. Our approach adopts a principled architecture for multitask learning to fold in the state-of-the-art models for each task. With a few more ingredients, e.g. orthography-sensitive input encoding and curriculum training, our model delivered significant improvements in all three tasks across all domains over strong baselines, including one using oracle prediction for domain detection, on real user data of a commercial personal assistant.