# Few-Shot Document-Level Event Argument Extraction

arXiv: https://arxiv.org/abs/2209.02203 · HTML: https://mlsanity.com/paper/2209.02203/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 52 references that are in the map.
- First posted on arXiv 6 Sep 2022.

## Builds on (most cited first, 21 of 21)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation](https://mlsanity.com/paper/1810.10147/) (2018)
- [DocRED: A Large-Scale Document-Level Relation Extraction Dataset](https://mlsanity.com/paper/1906.06127/) (2019)
- [Document-Level Event Argument Extraction by Conditional Generation](https://mlsanity.com/paper/2104.05919/) (2021)
- [Few-NERD: A Few-Shot Named Entity Recognition Dataset](https://mlsanity.com/paper/2105.07464/) (2021)
- [Transferrable Prototypical Networks for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1904.11227/) (2019)
- [Infinite Mixture Prototypes for Few-Shot Learning](https://mlsanity.com/paper/1902.04552/) (2019)
- [Multi-Sentence Argument Linking](https://mlsanity.com/paper/1911.03766/) (2019)
- [FLEX: Unifying Evaluation for Few-Shot NLP](https://mlsanity.com/paper/2107.07170/) (2021)
- [CONTaiNER: Few-Shot Named Entity Recognition via Contrastive Learning](https://mlsanity.com/paper/2109.07589/) (2021)
- [Doc2EDAG: An End-to-End Document-level Framework for Chinese Financial Event Extraction](https://mlsanity.com/paper/1904.07535/) (2019)
- [Meta-Learning with Dynamic-Memory-Based Prototypical Network for Few-Shot Event Detection](https://mlsanity.com/paper/1910.11621/) (2019)
- [Joint Extraction of Events and Entities within a Document Context](https://mlsanity.com/paper/1609.03632/) (2016)
- [Document-Level Event Role Filler Extraction using Multi-Granularity Contextualized Encoding](https://mlsanity.com/paper/2005.06579/) (2020)
- [Revisiting Few-shot Relation Classification: Evaluation Data and Classification Schemes](https://mlsanity.com/paper/2104.08481/) (2021)
- [Extensively Matching for Few-shot Learning Event Detection](https://mlsanity.com/paper/2006.10093/) (2020)
- [A Two-Stream AMR-enhanced Model for Document-level Event Argument Extraction](https://mlsanity.com/paper/2205.00241/) (2022)
- [Probing and Fine-tuning Reading Comprehension Models for Few-shot Event Extraction](https://mlsanity.com/paper/2010.11325/) (2020)
- [Few-Shot Document-Level Relation Extraction](https://mlsanity.com/paper/2205.02048/) (2022)

## Built on by (most cited first, 9 of 9)

- [TextEE: Benchmark, Reevaluation, Reflections, and Future Challenges in Event Extraction](https://mlsanity.com/paper/2311.09562/) (2023)
- [ULTRA: Unleash LLMs' Potential for Event Argument Extraction through Hierarchical Modeling and Pair-wise Self-Refinement](https://mlsanity.com/paper/2401.13218/) (2024)
- [LLMs Learn Task Heuristics from Demonstrations: A Heuristic-Driven Prompting Strategy for Document-Level Event Argument Extraction](https://mlsanity.com/paper/2311.06555/) (2023)
- [Linear Cross-document Event Coreference Resolution with X-AMR](https://mlsanity.com/paper/2404.08656/) (2024)
- [Event Extraction in Large Language Model](https://mlsanity.com/paper/2512.19537/) (2025)
- [Explaining Relationships Among Research Papers](https://mlsanity.com/paper/2402.13426/) (2024)
- [Improving Table Understanding with LLMs and Entity-Oriented Search](https://mlsanity.com/paper/2508.17028/) (2025)
- [Large Language Models for Document-Level Event-Argument Data Augmentation for Challenging Role Types](https://mlsanity.com/paper/2403.03304/) (2024)
- [Enhancing Document-level Argument Extraction with Definition-augmented Heuristic-driven Prompting for LLMs](https://mlsanity.com/paper/2409.00214/) (2024)

## Abstract (arXiv)

Event argument extraction (EAE) has been well studied at the sentence level but under-explored at the document level. In this paper, we study to capture event arguments that actually spread across sentences in documents. Prior works usually assume full access to rich document supervision, ignoring the fact that the available argument annotation is usually limited. To fill this gap, we present FewDocAE, a Few-Shot Document-Level Event Argument Extraction benchmark, based on the existing document-level event extraction dataset. We first define the new problem and reconstruct the corpus by a novel N -Way-D-Doc sampling instead of the traditional N -Way-K-Shot strategy. Then we adjust the current document-level neural models into the few-shot setting to provide baseline results under in- and cross-domain settings. Since the argument extraction depends on the context from multiple sentences and the learning process is limited to very few examples, we find this novel task to be very challenging with substantively low performance. Considering FewDocAE is closely related to practical use under low-resource regimes, we hope this benchmark encourages more research in this direction. Our data and codes will be available online.