Joint Information Extraction Across Classical and Modern Chinese with Tea-MOELoRA
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918138025607168 |
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| author | Tang, Xuemei Yan, Chengxi Gu, Jinghang Huang, Chu-Ren |
| author_facet | Tang, Xuemei Yan, Chengxi Gu, Jinghang Huang, Chu-Ren |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_01158 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Joint Information Extraction Across Classical and Modern Chinese with Tea-MOELoRA Tang, Xuemei Yan, Chengxi Gu, Jinghang Huang, Chu-Ren Computation and Language 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. |
| title | Joint Information Extraction Across Classical and Modern Chinese with Tea-MOELoRA |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2509.01158 |