# Large Language Models for Expansion of Spoken Language Understanding Systems to New Languages

arXiv: https://arxiv.org/abs/2404.02588 · HTML: https://mlsanity.com/paper/2404.02588/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Apr 2024.

## Built on by (most cited first, 2 of 2)

- [QUADS: QUAntized Distillation Framework for Efficient Speech Language Understanding](https://mlsanity.com/paper/2505.14723/) (2025)
- [Reasoning-Aware Query-Focused Summarization over Multi-Table Data](https://mlsanity.com/paper/2412.08970/) (2024)

## Abstract (arXiv)

Spoken Language Understanding (SLU) models are a core component of voice assistants (VA), such as Alexa, Bixby, and Google Assistant. In this paper, we introduce a pipeline designed to extend SLU systems to new languages, utilizing Large Language Models (LLMs) that we fine-tune for machine translation of slot-annotated SLU training data. Our approach improved on the MultiATIS++ benchmark, a primary multi-language SLU dataset, in the cloud scenario using an mBERT model. Specifically, we saw an improvement in the Overall Accuracy metric: from 53% to 62.18%, compared to the existing state-of-the-art method, Fine and Coarse-grained Multi-Task Learning Framework (FC-MTLF). In the on-device scenario (tiny and not pretrained SLU), our method improved the Overall Accuracy from 5.31% to 22.06% over the baseline Global-Local Contrastive Learning Framework (GL-CLeF) method. Contrary to both FC-MTLF and GL-CLeF, our LLM-based machine translation does not require changes in the production architecture of SLU. Additionally, our pipeline is slot-type independent: it does not require any slot definitions or examples.