# Capsule Networks for Low Resource Spoken Language Understanding

arXiv: https://arxiv.org/abs/1805.02922 · HTML: https://mlsanity.com/paper/1805.02922/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 May 2018.

## Built on by (most cited first, 11 of 11)

- [Leveraging Acoustic and Linguistic Embeddings from Pretrained speech and language Models for Intent Classification](https://mlsanity.com/paper/2102.07370/) (2021)
- [FANS: Fusing ASR and NLU for on-device SLU](https://mlsanity.com/paper/2111.00400/) (2021)
- [Bottleneck Low-rank Transformers for Low-resource Spoken Language Understanding](https://mlsanity.com/paper/2206.14318/) (2022)
- [Intent Recognition and Unsupervised Slot Identification for Low Resourced Spoken Dialog Systems](https://mlsanity.com/paper/2104.01287/) (2021)
- [Multitask Learning for Low Resource Spoken Language Understanding](https://mlsanity.com/paper/2211.13703/) (2022)
- [Pre-training for low resource speech-to-intent applications](https://mlsanity.com/paper/2103.16674/) (2021)
- [MSNER: A Multilingual Speech Dataset for Named Entity Recognition](https://mlsanity.com/paper/2405.11519/) (2024)
- [Exploring Teacher-Student Learning Approach for Multi-lingual Speech-to-Intent Classification](https://mlsanity.com/paper/2109.13486/) (2021)
- [Bidirectional Representations for Low Resource Spoken Language Understanding](https://mlsanity.com/paper/2211.14320/) (2022)
- [One-Shot Identification with Different Neural Network Approaches](https://mlsanity.com/paper/2601.08278/) (2026)
- [Multiple Additive Neural Networks for Structured and Unstructured Data](https://mlsanity.com/paper/2604.26888/) (2026)

## Abstract (arXiv)

Designing a spoken language understanding system for command-and-control applications can be challenging because of a wide variety of domains and users or because of a lack of training data. In this paper we discuss a system that learns from scratch from user demonstrations. This method has the advantage that the same system can be used for many domains and users without modifications and that no training data is required prior to deployment. The user is required to train the system, so for a user friendly experience it is crucial to minimize the required amount of data. In this paper we investigate whether a capsule network can make efficient use of the limited amount of available training data. We compare the proposed model to an approach based on Non-negative Matrix Factorisation which is the state-of-the-art in this setting and another deep learning approach that was recently introduced for end-to-end spoken language understanding. We show that the proposed model outperforms the baseline models for three command-and-control applications: controlling a small robot, a vocally guided card game and a home automation task.