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arXiv · 2509.01158

Joint Information Extraction Across Classical and Modern Chinese with Tea-MOELoRA

Xuemei Tang·Chengxi Yan·Jinghang Gu·Chu-Ren Huang
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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.

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Xuemei Tang, Chengxi Yan, Jinghang Gu, Chu-Ren Huang. 2025-09-01. Joint Information Extraction Across Classical and Modern Chinese with Tea-MOELoRA. https://arxiv.org/abs/2509.01158

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