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    <abstract>Spoken Language Understanding (SLU) is crucial for enabling natural voice interactions with modern devices. However, traditional supervised models fail to generalize to new domains due to two key challenges: the prohibitive cost of data annotation and the inherent difficulty of transferring domain-specific intents. While the rise of Large Language Models (LLMs) offers a promising solution through zero-shot inference, the zero-shot SLU capabilities of emerging speech-enabled LLMs have remained largely unexplored. To address this gap, this paper provides the first comprehensive assessment, focusing on intent classification (IC), the first key sub-task of SLU, across 13 languages. We systematically evaluate a range of architectures, including cascaded, end-to-end, and hybrid systems for zero-shot SLU. Our analysis identifies the hybrid approach as the most effective architectural design for end-to-end SLU, and assesses multilingual transfer capabilities. The findings offer a detailed map of the challenges and opportunities, highlighting which models and settings are most promising for zero-shot SLU.</abstract>
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%0 Conference Proceedings
%T Towards Zero-Shot SLU: An Empirical Study of Competing Architectural Paradigms
%A Lee, Beomseok
%A Gaido, Marco
%A Calapodescu, Ioan
%A Besacier, Laurent
%A Negri, Matteo
%Y Salesky, Elizabeth
%Y Anastasopoulos, Antonios
%Y Negri, Matteo
%Y Federico, Marcello
%S Proceedings of the 23rd International Conference on Spoken Language Translation (IWSLT 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, USA (in-person and online)
%@ 979-8-89176-411-8
%F lee-etal-2026-towards-zero
%X Spoken Language Understanding (SLU) is crucial for enabling natural voice interactions with modern devices. However, traditional supervised models fail to generalize to new domains due to two key challenges: the prohibitive cost of data annotation and the inherent difficulty of transferring domain-specific intents. While the rise of Large Language Models (LLMs) offers a promising solution through zero-shot inference, the zero-shot SLU capabilities of emerging speech-enabled LLMs have remained largely unexplored. To address this gap, this paper provides the first comprehensive assessment, focusing on intent classification (IC), the first key sub-task of SLU, across 13 languages. We systematically evaluate a range of architectures, including cascaded, end-to-end, and hybrid systems for zero-shot SLU. Our analysis identifies the hybrid approach as the most effective architectural design for end-to-end SLU, and assesses multilingual transfer capabilities. The findings offer a detailed map of the challenges and opportunities, highlighting which models and settings are most promising for zero-shot SLU.
%U https://aclanthology.org/2026.iwslt-1.1/
%P 1-7

Markdown (Informal)

[Towards Zero-Shot SLU: An Empirical Study of Competing Architectural Paradigms](https://aclanthology.org/2026.iwslt-1.1/) (Lee et al., IWSLT 2026)

• Towards Zero-Shot SLU: An Empirical Study of Competing Architectural Paradigms (Lee et al., IWSLT 2026)

ACL

• Beomseok Lee, Marco Gaido, Ioan Calapodescu, Laurent Besacier, and Matteo Negri. 2026. Towards Zero-Shot SLU: An Empirical Study of Competing Architectural Paradigms. In Proceedings of the 23rd International Conference on Spoken Language Translation (IWSLT 2026), pages 1–7, San Diego, USA (in-person and online). Association for Computational Linguistics.