# Multi-Domain Spoken Language Understanding Using Domain- and Task-Aware Parameterization

arXiv: https://arxiv.org/abs/2004.14871 · HTML: https://mlsanity.com/paper/2004.14871/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Apr 2020.

## Built on by (most cited first, 4 of 4)

- [A Survey on Spoken Language Understanding: Recent Advances and New Frontiers](https://mlsanity.com/paper/2103.03095/) (2021)
- [From Masked Language Modeling to Translation: Non-English Auxiliary Tasks Improve Zero-shot Spoken Language Understanding](https://mlsanity.com/paper/2105.07316/) (2021)
- [RETQA: A Large-Scale Open-Domain Tabular Question Answering Dataset for Real Estate Sector](https://mlsanity.com/paper/2412.10104/) (2024)
- [A Joint and Domain-Adaptive Approach to Spoken Language Understanding](https://mlsanity.com/paper/2107.11768/) (2021)

## Abstract (arXiv)

Spoken language understanding has been addressed as a supervised learning problem, where a set of training data is available for each domain. However, annotating data for each domain is both financially costly and non-scalable so we should fully utilize information across all domains. One existing approach solves the problem by conducting multi-domain learning, using shared parameters for joint training across domains. We propose to improve the parameterization of this method by using domain-specific and task-specific model parameters to improve knowledge learning and transfer. Experiments on 5 domains show that our model is more effective for multi-domain SLU and obtain the best results. In addition, we show its transferability by outperforming the prior best model by 12.4% when adapting to a new domain with little data.