Boundary-Aware LLM Augmentation for Low-Resource Event Argument Extraction

Challenge:	Event argument extraction (EAE) is a crucial task in information extraction but its performance heavily depends on expensive annotated data.
Approach:	They investigate argument replacement, adjunction rewriting, their combination, and annotation generation using four LLM-based augmentation strategies.
Outcome:	The proposed methods improve performance over boundary-agnostic methods and provide detailed analysis of quality from multiple perspectives.

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Overview

Challenge

Approach

Outcome

Document-Level Event-Argument Data Augmentation for Challenging Role Types (2025.acl-long)

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Joseph Gatto, Omar Sharif, Parker Seegmiller, Sarah Masud Preum

Responsible & Trustworthy NLP

Challenge:	Existing methods for Event Argument Extraction (EAE) are not well-suited to a variety of real-world situations, including long documents and challenging role types.
Approach:	They propose two novel methods for generating document-level EAE samples using zero in-domain training data and validate their generalizability.
Outcome:	The proposed methods show significant performance increases in low-resource settings.

Thinking about how to extract: Energizing LLMs’ emergence capabilities for document-level event argument extraction (2024.findings-acl)

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Kai Shuang, Zhouji Zhouji, Wang Qiwei, Jinyu Guo

Information Extraction & Text Mining

Challenge:	Existing models for document-level event argument extraction (D-EAE) lack key feature forgetting and cross-event argument confusion.
Approach:	They propose a document-level event argument extraction method based on guided summarization and reasoning that leverages the emergence capabilities of large language models to highlight key event information.
Outcome:	The proposed method outperforms baseline models by 1.3% F1 and 1.6% F1 on WIKIEVENTS and RAMS.

Can LLMs Augment Low-Resource Reading Comprehension Datasets? Opportunities and Challenges (2024.acl-srw)

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Vinay Samuel, Houda Aynaou, Arijit Chowdhury, Karthik Venkat Ramanan, Aman Chadha

Reasoning

Challenge:	Large Language Models (LLMs) have demonstrated impressive zero-shot performance on a wide range of NLP tasks.
Approach:	They propose to use large language models to augment extractive reading comprehension datasets by fine-tuning their annotations and comparing their performance to human annotators.
Outcome:	The proposed model can be used to augment extractive reading comprehension datasets.

Targeted Augmentation for Low-Resource Event Extraction (2024.findings-naacl)

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Sijia Wang, Lifu Huang

Information Extraction & Text Mining

Challenge:	Existing methods for low-resource information extraction struggle to strike a balance between weak augmentation and drastic augmentation.
Approach:	They propose a data augmentation paradigm that uses back validation and targeted augmentation to produce augmented examples with enhanced diversity, polarity, accuracy, and coherence.
Outcome:	The proposed paradigm produces augmented examples with enhanced diversity, polarity, accuracy, and coherence.

GENEVA: Benchmarking Generalizability for Event Argument Extraction with Hundreds of Event Types and Argument Roles (2023.acl-long)

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Tanmay Parekh, I-Hung Hsu, Kuan-Hao Huang, Kai-Wei Chang, Nanyun Peng

Semantic Text Processing

Challenge:	Existing benchmarking datasets for Event Argument Extraction (EAE) cover less than 40 event types and 25 entity-centric argument roles.
Approach:	They propose to use a large and diverse EAE ontology to create a semantic role labeling dataset for EAE that incorporates 115 events and 220 argument roles.
Outcome:	The proposed ontology concludes with 115 events and 220 argument roles, with a significant portion of roles not being entities.

ULTRA: Unleash LLMs’ Potential for Event Argument Extraction through Hierarchical Modeling and Pair-wise Self-Refinement (2024.findings-acl)

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Xinliang Frederick Zhang, Carter Blum, Temma Choji, Shalin Shah, Alakananda Vempala

Information Extraction & Text Mining

Challenge:	Structural extraction of events within discourse is critical for event-centric understanding . document-level EAE focuses on arguments that are scattered across an entire document . ULTRA is a hierarchical framework that extracts event arguments more cost-effectively .
Approach:	They propose a hierarchical framework that extracts event arguments more cost-effectively . ULTRA sequentially reads text chunks of a document to generate a candidate argument set . they propose to use a supervised model to find the exact boundary of an argument .
Outcome:	The proposed framework outperforms strong models and ChatGPT by 9.8% when evaluated by Exact Match (EM).

Resource-Enhanced Neural Model for Event Argument Extraction (2020.findings-emnlp)

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Jie Ma, Shuai Wang, Rishita Anubhai, Miguel Ballesteros, Yaser Al-Onaizan

Information Extraction & Text Mining

Challenge:	Existing work on event argument extraction (EE) is limited due to data scarcity and lack of a model encoder.
Approach:	They propose to capture the long-range dependency between an event trigger and a distant event argument using unlabeled data.
Outcome:	Experiments on the English ACE 2005 benchmark show that the proposed method achieves a new state-of-the-art.

AMPERE: AMR-Aware Prefix for Generation-Based Event Argument Extraction Model (2023.acl-long)

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I-Hung Hsu, Zhiyu Xie, Kuan-Hao Huang, Prem Natarajan, Nanyun Peng

Information Extraction & Text Mining

Challenge:	Existing generation-based EAE models focus on problem re-formulation and prompt design without incorporating additional information that has been shown to be effective for classification-based models.
Approach:	They propose to incorporate AMR into generation-based EAE models by generating AMR-aware prefixes for every layer of the generation model.
Outcome:	The proposed model generates AMR-aware prefixes for every layer of the generation model and improves the generation.

EA2E: Improving Consistency with Event Awareness for Document-Level Argument Extraction (2022.findings-naacl)

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Qi Zeng, Qiusi Zhan, Heng Ji

Reasoning

Challenge:	Recent work on document-level event argument extraction models each individual event in isolation and therefore causes inconsistency among extracted arguments across events.
Approach:	They propose an event-aware argument extraction model with augmented context to improve consistency . they hypothesize that participants tend to play consistent roles across multiple events in a document .
Outcome:	The proposed model improves consistency and accuracy of arguments extracted from documents.

Empowering Large Language Models for Textual Data Augmentation (2024.findings-acl)

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Yichuan Li, Kaize Ding, Jianling Wang, Kyumin Lee

Language Models

Challenge:	True. True. False
Approach:	False slants are proposed to generate a large pool of augmentation instructions and select the most suitable task-informed instructions.
Outcome:	False omissions: the proposed approach consistently generates augmented data with better quality compared to non-LLM and LLM-based data augmentation methods.