@inproceedings{yang-etal-2026-learnable,
title = "A Learnable Skill Combination Strategy for Multi-task Learning in Natural Language Understanding",
author = "Yang, Zhe and
Huang, Yi and
Chen, Yaqin and
Guo, Mengfei and
Wu, Xiaoting and
Feng, Junlan",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1886/",
pages = "37839--37845",
ISBN = "979-8-89176-395-1",
abstract = "In the realm of domain-specific natural language understanding (NLU) tasks, acquiring high-quality labeled data is often arduous, thereby posing significant challenges for effective model training. Multi-task learning (MTL) addresses these limitations by jointly optimizing multiple tasks within a unified framework. In this paper, we introduce a novel sparse NLU multi-task learning framework that decomposes the language model into modular skill components and employs a dynamic, learnable skill-combination mechanism to adaptively handle diverse tasks. Extensive experiments on benchmark NLU datasets demonstrate that our proposed method surpasses conventional multi-task learning approaches in performance."
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<abstract>In the realm of domain-specific natural language understanding (NLU) tasks, acquiring high-quality labeled data is often arduous, thereby posing significant challenges for effective model training. Multi-task learning (MTL) addresses these limitations by jointly optimizing multiple tasks within a unified framework. In this paper, we introduce a novel sparse NLU multi-task learning framework that decomposes the language model into modular skill components and employs a dynamic, learnable skill-combination mechanism to adaptively handle diverse tasks. Extensive experiments on benchmark NLU datasets demonstrate that our proposed method surpasses conventional multi-task learning approaches in performance.</abstract>
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%0 Conference Proceedings
%T A Learnable Skill Combination Strategy for Multi-task Learning in Natural Language Understanding
%A Yang, Zhe
%A Huang, Yi
%A Chen, Yaqin
%A Guo, Mengfei
%A Wu, Xiaoting
%A Feng, Junlan
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F yang-etal-2026-learnable
%X In the realm of domain-specific natural language understanding (NLU) tasks, acquiring high-quality labeled data is often arduous, thereby posing significant challenges for effective model training. Multi-task learning (MTL) addresses these limitations by jointly optimizing multiple tasks within a unified framework. In this paper, we introduce a novel sparse NLU multi-task learning framework that decomposes the language model into modular skill components and employs a dynamic, learnable skill-combination mechanism to adaptively handle diverse tasks. Extensive experiments on benchmark NLU datasets demonstrate that our proposed method surpasses conventional multi-task learning approaches in performance.
%U https://aclanthology.org/2026.findings-acl.1886/
%P 37839-37845
Markdown (Informal)
[A Learnable Skill Combination Strategy for Multi-task Learning in Natural Language Understanding](https://aclanthology.org/2026.findings-acl.1886/) (Yang et al., Findings 2026)
ACL