A Unified Event Argument Extraction Framework with Layered Attention and Retrieval-Aware Prompting
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Abstract
Event argument extraction is a critical subtask of information extraction that aims to identify and classify entities that participate in specific events mentioned in unstructured text. Despite recent advancements in natural language processing, extracting arguments accurately remains challenging due to the complex syntactic structures of long sentences, the implicit relationships between event triggers and arguments, and the scarcity of high-quality annotated data. To address these persistent challenges, this paper proposes a novel, unified event argument extraction framework that synergistically integrates a layered attention mechanism with a retrieval-aware prompting strategy. The layered attention module is designed to capture hierarchical structural semantics from word-level to sentence-level representations, enabling the model to dynamically focus on contextually relevant spans while ignoring noisy syntactic artifacts. Concurrently, the retrieval-aware prompting module leverages a dense retrieval system to fetch structurally similar historical event templates from an external datastore. These retrieved instances are then seamlessly integrated into the prompt formulation process, providing explicit grounding and guiding the generative language model to produce accurate argument roles. Extensive evaluations on standard benchmark datasets demonstrate the superiority of the proposed framework over existing state-of-the-art baselines. Through comprehensive ablation studies and error analyses, we reveal that the combination of hierarchical context modeling and retrieval-augmented guidance significantly mitigates issues related to long-range dependencies and out-of-domain generalization.
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References
1. Yang, Huan, et al. "Kvshare: An llm service system with efficient and effective multi-tenant kv cache reuse." arXiv preprint arXiv:2503.16525 (2025).
2. Chen, L. (2026). Beyond external constraints: The missing dimension of ai governance. Available at SSRN 6449738.
3. Dong, J., Wang, Y., Lai, J. H., & Xie, X. (2022). Improving adversarially robust few-shot image classification with generalizable representations. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 9025-9034).
4. Li, W. W., Ziser, Y., Coavoux, M., & Cohen, S. B. (2023, May). BERT is not the count: Learning to match mathematical statements with proofs. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics (pp. 3581-3593).
5. Li, Weixian Waylon, et al. "Time is Not a Label: Continuous Phase Rotation for Temporal Knowledge Graphs and Agentic Memory." arXiv preprint arXiv:2604.11544 (2026).
6. Liu, J., Zhang, Y., Sheng, Y., Lou, Y., Wang, H., & Yang, B. (2025, March). Context-aware rule mining using a dynamic transformer-based framework. In 2025 8th International Conference on Advanced Algorithms and Control Engineering (ICAACE) (pp. 2047-2052). IEEE.
7. Xu, X. (2024). Improving monaural speech enhancement by mapping to fixed simulation space with knowledge distillation. IEEE Signal Processing Letters, 31, 386-390.
8. Zhou, J., Shuang, K., An, Z., Guo, J., & Loo, J. (2023). Improving document-level event detection with event relation graph. Information Sciences, 645, 119355.
9. Li, J., Zeng, P., & Luo, P. (2026). CANAO: A Cloud-Aware Native Agentic AI Framework for Adaptive Task Orchestration in Cloud-Native Environments. Frontiers in Artificial Intelligence Research, 3(1), 187-198.
10. Xue, X., Wang, S., Yao, H. D., Davidson, L., & Coveney, P. V. (2024). Physics informed data-driven near-wall modelling for lattice Boltzmann simulation of high Reynolds number turbulent flows. Communications Physics, 7(1), 338.
11. Kong, R., Li, Y., Feng, Q., Wang, W., Ye, X., Ouyang, Y., ... & Liu, Y. (2024, August). SwapMoE: Serving off-the-shelf MoE-based large language models with tunable memory budget. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 6710-6720).
12. Guo, J., Wang, Z., Pu, J., Tian, W., Duan, G., & Luo, G. (2025). Multi-Perspective Dialogue Non-Quota Selection with loss monitoring for dialogue state tracking. Expert Systems with Applications, 283, 127516.
13. Qian, B., Guo, J., Wang, Q., & Shuang, K. (2025). From local verification to global reasoning: Exploiting slot-accompanying update for improved slot selection. Knowledge-Based Systems, 319, 113521.
14. Xue, X., Yao, H. D., & Davidson, L. (2022). Synthetic turbulence generator for lattice Boltzmann method at the interface between RANS and LES. Physics of Fluids, 34(5).
15. Banerjee, S., & Lavie, A. (2005). METEOR: An automatic metric for MT evaluation with improved correlation with human judgments. In Proceedings of the ACL Workshop on Intrinsic and Extrinsic Evaluation Measures for MT and/or Summarization.
16. Ren, Y., Wu, Y., Fan, J. W., Khurana, A., Fu, S., Wu, D., ... & Huang, M. (2024). Automatic uncovering of patient primary concerns in portal messages using a fusion framework of pretrained language models. Journal of the American Medical Informatics Association, 31(8), 1714-1724.
17. Li, Weixian Waylon, et al. "Spectral attention steering for prompt highlighting." arXiv preprint arXiv:2603.01281 (2026).
18. Zhang, Y., Valentino, M., Carvalho, D., Pratt-Hartmann, I., & Freitas, A. (2024, June). Graph-Induced Syntactic-Semantic Spaces in Transformer-Based Variational AutoEncoders. In Findings of the Association for Computational Linguistics: NAACL 2024 (pp. 474-489).
19. Dong, J., Koniusz, P., Chen, J., & Ong, Y. S. (2024, September). Adversarially robust distillation by reducing the student-teacher variance gap. In European Conference on Computer Vision (pp. 92-111). Cham: Springer Nature Switzerland.
20. Zhu, Guoying, et al. "Enabling MoE on the Edge via Importance-Driven Expert Scheduling." arXiv preprint arXiv:2508.18983 (2025).
21. Li, W. W., Ziser, Y., Xie, Y., Cohen, S. B., & Ma, T. (2025, July). Tsprank: Bridging pairwise and listwise methods with a bilinear travelling salesman model. In Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V. 1 (pp. 707-718).
22. Wang, S., Yu, Y., Feldt, R., & Parthasarathy, D. (2025). Automating a complete software test process using llms: An automotive case study. arXiv preprint arXiv:2502.04008.
23. Xu, X., Han, C., Zhang, Y., Tu, W., & Yang, Y. (2024, April). Curricular Contrastive Regularization for Speech Enhancement with Self-Supervised Representations. In ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 10486-10490). IEEE.
24. Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of NAACL-HLT.
25. Zhang, Y., Carvalho, D., & Freitas, A. (2025, July). Quasi-symbolic Semantic Geometry over Transformer-based Variational AutoEncoder. In Proceedings of the 29th Conference on Computational Natural Language Learning (pp. 12-29).
26. Xue, Xiao, et al. "Equivariant U-Shaped Neural Operators for the Cahn-Hilliard Phase-Field Model." arXiv preprint arXiv:2509.01293 (2025).
27. Wang J, Fan L, Li B, et al. A Dynamic Factor Gating Architecture with Market Regime Awareness for Stock Return Forecasting[J]. 2026.
28. Zhang, W., Zhang, C., Gu, C., Kou, J., Yuan, H., Fang, X., ... & Fang, Y. (2024, October). Hallucination in Large Language Models: From Mechanistic Understanding to Novel Control Frameworks. In 2024 7th International Conference on Universal Village (UV) (pp. 1-36). IEEE.
29. Qu, W., Shao, Y., Meng, L., Huang, X., & Xiao, L. (2024). A conditional denoising diffusion probabilistic model for point cloud upsampling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 20786-20795).
30. Wu, Beiliang, et al. "IndexNet: Timestamp and Variable-Aware Modeling for Time Series Forecasting." arXiv preprint arXiv:2509.23813 (2025).
31. Zhang, W., Zhang, C., Luo, Z., Ma, J., Yuan, W., Gu, C., & Feng, C. (2025). SemanticForge: Repository-Level Code Generation through Semantic Knowledge Graphs and Constraint Satisfaction. arXiv preprint arXiv:2511.07584.
32. Tu, P., Huang, Y., Zheng, F., He, Z., Cao, L., & Shao, L. (2022, June). Guidedmix-net: Semi-supervised semantic segmentation by using labeled images as reference. In Proceedings of the AAAI conference on artificial intelligence (Vol. 36, No. 2, pp. 2379-2387).
33. Dong, J., Yang, L., Wang, Y., Xie, X., & Lai, J. (2023). Toward intrinsic adversarial robustness through probabilistic training. IEEE Transactions on Image Processing, 32, 3862-3872.
34. Tang, Y., Zhang, G., Liu, J. K., & Qin, R. (2025). Weakly supervised land-cover classification of high-resolution images with low-resolution labels through optimized label refinement. International Journal of Remote Sensing, 46(5), 1913-1937.
35. Luong, M.-T., Pham, H., & Manning, C. D. (2015). Effective approaches to attention-based neural machine translation. In Proceedings of EMNLP.
36. Wang, J., Zhao, R., Wei, W., Wang, Y., Yu, M., Zhou, J., ... & Xu, L. (2026, March). Comorag: A cognitive-inspired memory-organized rag for stateful long narrative reasoning. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 40, No. 39, pp. 33557-33565).
37. Huang, Jinbang, et al. "One Demo Is All It Takes: Planning Domain Derivation with LLMs from A Single Demonstration." arXiv preprint arXiv:2505.18382 (2025).
38. Xue, Xiao, et al. "Uni-Flow: a unified autoregressive-diffusion model for complex multiscale flows." arXiv preprint arXiv:2602.15592 (2026).