Element relational graph-augmented multi-granularity contextualized encoding for document-level event role filler extraction

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References

[1] Du X, Cardie C. Document-level event role filler extraction using multi-granularity contextualized encoding. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 2020, 8010–8020

[2] McLean V. Fourth message understanding conference (MUC-4). In: Proceedings of Fourth Message Understanding Conference (MUC-4). 1992

[3] Wadden D, Wennberg U, Luan Y, Hajishirzi H. Entity, relation, and event extraction with contextualized span representations. In: Proceedings of 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing. 2019, 5784–5789

[4] Du X, Rush A M, Cardie C. GRIT: Generative role-filler transformers for document-level event entity extraction. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics. 2021, 634–644

[5] Paolini G, Athiwaratkun B, Krone J, Jie M, Achille A, Anubhai R, dos Santos C N, Xiang B, Soatto S. Structured prediction as translation between augmented natural languages. In: Proceedings of the 9th International Conference on Learning Representations. 2021, 1–26

[6] Huang K H, Tang S, Peng N. Document-level entity-based extraction as template generation. In: Proceedings of 2021 Conference on Empirical Methods in Natural Language Processing. 2021, 5257–5269

[7] Chen Y, Gantt W, Gu W, Chen T, White A, Van Durme B. Iterative document-level information extraction via imitation learning. In: Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics. 2023, 1858–1874

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