A Learnable Skill Combination Strategy for Multi-task Learning in Natural Language Understanding
Year: 2026
Type: conference-paper
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 skillcombination mechanism to adaptively handle diverse tasks.Extensive experiments on benchmark NLU datasets demonstrate that our proposed method surpasses conventional multitask learning approaches in performance.
Institution: Tsinghua University
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166841113