# Resource-Enhanced Neural Model for Event Argument Extraction

arXiv: https://arxiv.org/abs/2010.03022 · HTML: https://mlsanity.com/paper/2010.03022/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Oct 2020.

## Built on by (most cited first, 15 of 15)

- [Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extraction](https://mlsanity.com/paper/2106.09232/) (2021)
- [AMPERE: AMR-Aware Prefix for Generation-Based Event Argument Extraction Model](https://mlsanity.com/paper/2305.16734/) (2023)
- [The Devil is in the Details: On the Pitfalls of Event Extraction Evaluation](https://mlsanity.com/paper/2306.06918/) (2023)
- [A Two-Stream AMR-enhanced Model for Document-level Event Argument Extraction](https://mlsanity.com/paper/2205.00241/) (2022)
- [Event Extraction as Question Generation and Answering](https://mlsanity.com/paper/2307.05567/) (2023)
- [TAGPRIME: A Unified Framework for Relational Structure Extraction](https://mlsanity.com/paper/2205.12585/) (2022)
- [Sequential Cross-Document Coreference Resolution](https://mlsanity.com/paper/2104.08413/) (2021)
- [An AMR-based Link Prediction Approach for Document-level Event Argument Extraction](https://mlsanity.com/paper/2305.19162/) (2023)
- [Improving Zero-Shot Event Extraction via Sentence Simplification](https://mlsanity.com/paper/2204.02531/) (2022)
- [Bi-Directional Iterative Prompt-Tuning for Event Argument Extraction](https://mlsanity.com/paper/2210.15843/) (2022)
- [Event-enhanced Retrieval in Real-time Search](https://mlsanity.com/paper/2404.05989/) (2024)
- [PoKE: A Prompt-based Knowledge Eliciting Approach for Event Argument Extraction](https://mlsanity.com/paper/2109.05190/) (2021)
- [PESE: Event Structure Extraction using Pointer Network based Encoder-Decoder Architecture](https://mlsanity.com/paper/2211.12157/) (2022)
- ['If you build they will come': Automatic Identification of News-Stakeholders to detect Party Preference in News Coverage](https://mlsanity.com/paper/2212.08864/) (2022)
- [On the Visualisation of Argumentation Graphs to Support Text Interpretation](https://mlsanity.com/paper/2303.03235/) (2023)

## Abstract (arXiv)

Event argument extraction (EAE) aims to identify the arguments of an event and classify the roles that those arguments play. Despite great efforts made in prior work, there remain many challenges: (1) Data scarcity. (2) Capturing the long-range dependency, specifically, the connection between an event trigger and a distant event argument. (3) Integrating event trigger information into candidate argument representation. For (1), we explore using unlabeled data in different ways. For (2), we propose to use a syntax-attending Transformer that can utilize dependency parses to guide the attention mechanism. For (3), we propose a trigger-aware sequence encoder with several types of trigger-dependent sequence representations. We also support argument extraction either from text annotated with gold entities or from plain text. Experiments on the English ACE2005 benchmark show that our approach achieves a new state-of-the-art.