Joint Information Extraction Across Classical and Modern Chinese with Tea-MOELoRA
Sep 3, 2025·
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1 min read
TANG Xuemei (唐雪梅)
Chengxi Yan
Jinggang Gu
Chu-Ren Huang
Image credit: Unsplash
Abstract
Chinese information extraction (IE) involves multiple tasks across diverse temporal domains, including Classical and Modern documents. Fine-tuning a single model on heterogeneous tasks and across different eras may lead to interference and reduced performance. Therefore, in this paper, we propose Tea-MOELoRA, a parameter-efficient multi-task framework that combines LoRA with a Mixture-of-Experts (MoE) design. Multiple low-rank LoRA experts specialize in different IE tasks and eras, while a task-era-aware router mechanism dynamically allocates expert contributions. Experiments show that Tea-MOELoRA outperforms both single-task and joint LoRA baselines, demonstrating its ability to leverage task and temporal knowledge effectively.
Type
Publication
arXiv:2509.01158
This work is driven by the results in my previous paper on LLMs.
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