@inproceedings{wu-etal-2025-int,
title = "{INT}: Establishing Information Transfer for Multilingual Intent Detection and Slot Filling",
author = "Wu, Di and
Jiang, Liting and
Mao, Bohui and
Xie, Hongyan and
Su, Haoxiang and
He, Zhongjiang and
Fang, Ruiyu and
Song, Shuangyong and
Huang, Hao and
Li, Xuelong",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.783/",
doi = "10.18653/v1/2025.findings-acl.783",
pages = "15120--15142",
ISBN = "979-8-89176-256-5",
abstract = "Multilingual spoken language understanding (SLU) involves intent detection (ID) and slot filling (SF) across multiple languages. The inherent linguistic diversity presents significant challenges in achieving performance comparable to traditional SLU. Recent studies have attempted to improve multilingual SLU performance by sharing multilingual encoders. However, these approaches have not directly established information flow between languages. To address this, we first demonstrate the feasibility of such information transfer and pinpoint the key challenges: prediction error mitigation and multilingual slot alignment. We then propose the INformation Transfer network (INT) to tackle these challenges. The gate unit in INT controls the information flow between languages, reducing the adverse impact of prediction errors on both ID and SF. Additionally, we reformulate SF as a span prediction problem and introduce a slot-matching attention mechanism to achieve slot alignment across languages. Experimental results on the MASSIVE and MASSIVE-UG datasets show that our model outperforms all baselines in overall accuracy across all languages, and demonstrates robust performance when different languages are used as the source."
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<abstract>Multilingual spoken language understanding (SLU) involves intent detection (ID) and slot filling (SF) across multiple languages. The inherent linguistic diversity presents significant challenges in achieving performance comparable to traditional SLU. Recent studies have attempted to improve multilingual SLU performance by sharing multilingual encoders. However, these approaches have not directly established information flow between languages. To address this, we first demonstrate the feasibility of such information transfer and pinpoint the key challenges: prediction error mitigation and multilingual slot alignment. We then propose the INformation Transfer network (INT) to tackle these challenges. The gate unit in INT controls the information flow between languages, reducing the adverse impact of prediction errors on both ID and SF. Additionally, we reformulate SF as a span prediction problem and introduce a slot-matching attention mechanism to achieve slot alignment across languages. Experimental results on the MASSIVE and MASSIVE-UG datasets show that our model outperforms all baselines in overall accuracy across all languages, and demonstrates robust performance when different languages are used as the source.</abstract>
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%0 Conference Proceedings
%T INT: Establishing Information Transfer for Multilingual Intent Detection and Slot Filling
%A Wu, Di
%A Jiang, Liting
%A Mao, Bohui
%A Xie, Hongyan
%A Su, Haoxiang
%A He, Zhongjiang
%A Fang, Ruiyu
%A Song, Shuangyong
%A Huang, Hao
%A Li, Xuelong
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F wu-etal-2025-int
%X Multilingual spoken language understanding (SLU) involves intent detection (ID) and slot filling (SF) across multiple languages. The inherent linguistic diversity presents significant challenges in achieving performance comparable to traditional SLU. Recent studies have attempted to improve multilingual SLU performance by sharing multilingual encoders. However, these approaches have not directly established information flow between languages. To address this, we first demonstrate the feasibility of such information transfer and pinpoint the key challenges: prediction error mitigation and multilingual slot alignment. We then propose the INformation Transfer network (INT) to tackle these challenges. The gate unit in INT controls the information flow between languages, reducing the adverse impact of prediction errors on both ID and SF. Additionally, we reformulate SF as a span prediction problem and introduce a slot-matching attention mechanism to achieve slot alignment across languages. Experimental results on the MASSIVE and MASSIVE-UG datasets show that our model outperforms all baselines in overall accuracy across all languages, and demonstrates robust performance when different languages are used as the source.
%R 10.18653/v1/2025.findings-acl.783
%U https://aclanthology.org/2025.findings-acl.783/
%U https://doi.org/10.18653/v1/2025.findings-acl.783
%P 15120-15142
Markdown (Informal)
[INT: Establishing Information Transfer for Multilingual Intent Detection and Slot Filling](https://aclanthology.org/2025.findings-acl.783/) (Wu et al., Findings 2025)
ACL
- Di Wu, Liting Jiang, Bohui Mao, Hongyan Xie, Haoxiang Su, Zhongjiang He, Ruiyu Fang, Shuangyong Song, Hao Huang, and Xuelong Li. 2025. INT: Establishing Information Transfer for Multilingual Intent Detection and Slot Filling. In Findings of the Association for Computational Linguistics: ACL 2025, pages 15120–15142, Vienna, Austria. Association for Computational Linguistics.