# Document-Level Event Role Filler Extraction using Multi-Granularity Contextualized Encoding

arXiv: https://arxiv.org/abs/2005.06579 · HTML: https://mlsanity.com/paper/2005.06579/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 May 2020.

## Built on by (most cited first, 28 of 28)

- [Document-level Entity-based Extraction as Template Generation](https://mlsanity.com/paper/2109.04901/) (2021)
- [Document-level Event Extraction via Heterogeneous Graph-based Interaction Model with a Tracker](https://mlsanity.com/paper/2105.14924/) (2021)
- [Textual Entailment for Event Argument Extraction: Zero- and Few-Shot with Multi-Source Learning](https://mlsanity.com/paper/2205.01376/) (2022)
- [The Devil is in the Details: On the Pitfalls of Event Extraction Evaluation](https://mlsanity.com/paper/2306.06918/) (2023)
- [Retrieval-Augmented Generative Question Answering for Event Argument Extraction](https://mlsanity.com/paper/2211.07067/) (2022)
- [A Two-Stream AMR-enhanced Model for Document-level Event Argument Extraction](https://mlsanity.com/paper/2205.00241/) (2022)
- [CasEE: A Joint Learning Framework with Cascade Decoding for Overlapping Event Extraction](https://mlsanity.com/paper/2107.01583/) (2021)
- [A Survey on Deep Learning Event Extraction: Approaches and Applications](https://mlsanity.com/paper/2107.02126/) (2021)
- [ULTRA: Unleash LLMs' Potential for Event Argument Extraction through Hierarchical Modeling and Pair-wise Self-Refinement](https://mlsanity.com/paper/2401.13218/) (2024)
- [EA2E: Improving Consistency with Event Awareness for Document-Level Argument Extraction](https://mlsanity.com/paper/2205.14847/) (2022)
- [Open-Vocabulary Argument Role Prediction for Event Extraction](https://mlsanity.com/paper/2211.01577/) (2022)
- [A Unified View of Evaluation Metrics for Structured Prediction](https://mlsanity.com/paper/2310.13793/) (2023)
- [Enhancing Document-level Event Argument Extraction with Contextual Clues and Role Relevance](https://mlsanity.com/paper/2310.05991/) (2023)
- [Few-Shot Document-Level Event Argument Extraction](https://mlsanity.com/paper/2209.02203/) (2022)
- [An AMR-based Link Prediction Approach for Document-level Event Argument Extraction](https://mlsanity.com/paper/2305.19162/) (2023)
- [LLMs Learn Task Heuristics from Demonstrations: A Heuristic-Driven Prompting Strategy for Document-Level Event Argument Extraction](https://mlsanity.com/paper/2311.06555/) (2023)
- [CUP: Curriculum Learning based Prompt Tuning for Implicit Event Argument Extraction](https://mlsanity.com/paper/2205.00498/) (2022)
- [MOKA: Moral Knowledge Augmentation for Moral Event Extraction](https://mlsanity.com/paper/2311.09733/) (2023)
- [Sketching as a Tool for Understanding and Accelerating Self-attention for Long Sequences](https://mlsanity.com/paper/2112.05359/) (2021)
- [Event Extraction in Large Language Model](https://mlsanity.com/paper/2512.19537/) (2025)
- [Asking the Right Questions in Low Resource Template Extraction](https://mlsanity.com/paper/2205.12643/) (2022)
- [Event Causality Extraction with Event Argument Correlations](https://mlsanity.com/paper/2301.11621/) (2023)
- [Event Extraction: A Survey](https://mlsanity.com/paper/2210.03419/) (2022)
- [Leveraging Codebook Knowledge with NLI and ChatGPT for Zero-Shot Political Relation Classification](https://mlsanity.com/paper/2308.07876/) (2023)
- [Document-Level Event Extraction via Human-Like Reading Process](https://mlsanity.com/paper/2202.03092/) (2022)
- [SciEvent: Benchmarking Multi-domain Scientific Event Extraction](https://mlsanity.com/paper/2509.15620/) (2025)
- [Token-Event-Role Structure-based Multi-Channel Document-Level Event Extraction](https://mlsanity.com/paper/2306.17733/) (2023)
- [A Multimodal Text- and Graph-Based Approach for Open-Domain Event Extraction from Documents](https://mlsanity.com/paper/2604.21885/) (2026)

## Abstract (arXiv)

Few works in the literature of event extraction have gone beyond individual sentences to make extraction decisions. This is problematic when the information needed to recognize an event argument is spread across multiple sentences. We argue that document-level event extraction is a difficult task since it requires a view of a larger context to determine which spans of text correspond to event role fillers. We first investigate how end-to-end neural sequence models (with pre-trained language model representations) perform on document-level role filler extraction, as well as how the length of context captured affects the models' performance. To dynamically aggregate information captured by neural representations learned at different levels of granularity (e.g., the sentence- and paragraph-level), we propose a novel multi-granularity reader. We evaluate our models on the MUC-4 event extraction dataset, and show that our best system performs substantially better than prior work. We also report findings on the relationship between context length and neural model performance on the task.