{"ID":2875747,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.01158","arxiv_id":"2509.01158","title":"Joint Information Extraction Across Classical and Modern Chinese with Tea-MOELoRA","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.","short_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 paramet...","url_abs":"https://arxiv.org/abs/2509.01158","url_pdf":"https://arxiv.org/pdf/2509.01158v3","authors":"[\"Xuemei Tang\",\"Chengxi Yan\",\"Jinghang Gu\",\"Chu-Ren Huang\"]","published":"2025-09-01T06:28:33Z","proceeding":"cs.CL","tasks":"[\"cs.CL\"]","methods":"[\"LoRA\"]","has_code":false}