EVENT EXTRACTION

NoEl: An Annotated Corpus for Noun Ellipsis in English (2020.lrec-1)

Challenge:	Existing studies mainly adopt coarse-grained events, which loses the specific semantic information of diverse event types.
Approach:	They propose to use a finance event dictionary to extract fine-grained events from finance news to train a neural model that uses the extracted events as the distant supervised label to train stock prediction.
Outcome:	The proposed method outperforms baselines and has good generalizability.

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Payal Khullar, Kushal Majmundar, Manish Shrivastava

Dialogue Systems & Conversational Agents

Challenge:	Ellipsis resolution is an important step to improve the accuracy of mainstream natural language processing tasks such as information retrieval, event extraction, dialog systems, etc.
Approach:	They extend the study of ellipsis by annotating a corpus for noun ellippsis and closely related phenomenon using the first hundred movies of Cornell Movie Dialogs Dataset.
Outcome:	The proposed corpus has 946 instances of exophoric and endophorical noun ellipsis, making it the biggest resource of nouns in English, to the best of our knowledge.

JSEEGraph: Joint Structured Event Extraction as Graph Parsing (2023.starsem-1)

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Huiling You, Lilja Vrelid, Samia Touileb

CogIE: An Information Extraction Toolkit for Bridging Texts and CogNet (2021.acl-demo)

Challenge:	Existing approaches model event extraction using simplified datasets or sequence-labeling-based encodings.
Approach:	They propose a graph-based event extraction framework that explicitly encodes entities and events in a single semantic graph.
Outcome:	The proposed framework can handle nested event structures and solve different IE tasks jointly.

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Zhuoran Jin, Yubo Chen, Dianbo Sui, Chenhao Wang, Zhipeng Xue, Jun Zhao

CasEE: A Joint Learning Framework with Cascade Decoding for Overlapping Event Extraction (2021.findings-acl)

Challenge:	CogNet is a knowledge base that integrates three types of knowledge: linguistic knowledge, world knowledge and commonsense knowledge.
Approach:	They propose an information extraction toolkit called CogIE that is a bridge connecting raw texts and CogNet.
Outcome:	The proposed toolkit can ground raw texts to CogNet and leverage different types of knowledge to enrich extracted results.

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Jiawei Sheng, Shu Guo, Bowen Yu, Qian Li, Yiming Hei, Lihong Wang, Tingwen Liu, Hongbo Xu

CEAN: Contrastive Event Aggregation Network with LLM-based Augmentation for Event Extraction (2024.eacl-long)

Challenge:	Existing methods assume that events appear in sentences without overlaps . overlapping event extraction is a challenging task in natural language understanding .
Approach:	They propose a joint learning framework with cascade decoding for overlapping event extraction . they sequentially perform type detection, trigger extraction and argument extraction based on the specific former prediction .
Outcome:	The proposed framework improves on a public event extraction benchmark . it sequentially performs type detection, trigger extraction and argument extraction .

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Zihao Meng, Tao Liu, Heng Zhang, Kai Feng, Peng Zhao

Multilingual Entity, Relation, Event and Human Value Extraction (N19-4)

Challenge:	Event Extraction is a crucial yet arduous task in natural language processing (NLP), as its performance is hindered by laborious data annotation.
Approach:	They propose a Contrastive Event Aggregation Network with LLM-based Augmentation to promote low-resource learning and reduce data noise for event extraction.
Outcome:	The proposed approach achieves new state-of-the-art results on the ACE2005 and ERE-EN datasets.

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Manling Li, Ying Lin, Joseph Hoover, Spencer Whitehead, Clare Voss, Morteza Dehghani, Heng Ji

Neural Gibbs Sampling for Joint Event Argument Extraction (2020.aacl-main)

Challenge:	Existing systems that extract knowledge elements from multiple languages and documents do not aggregate knowledge from multiple documents and languages.
Approach:	They propose a multilingual knowledge extraction system that performs entity discovery and linking, relation extraction, event extraction, and coreference.
Outcome:	The proposed system performs entity discovery and linking, relation extraction, event extraction, and coreference.

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Xiaozhi Wang, Shengyu Jia, Xu Han, Zhiyuan Liu, Juanzi Li, Peng Li, Jie Zhou

Exploring Interpretability in Event Extraction: Multitask Learning of a Neural Event Classifier and an Explanation Decoder (2020.acl-srw)

Challenge:	Existing methods for event argument extraction cannot adequately model the correlation between event arguments and their roles.
Approach:	They propose a Bayesian model to jointly extract event arguments using Gibbs sampling . they train two neural networks to model prior distribution and conditional distribution over event arguments .
Outcome:	The proposed model can achieve comparable results to existing methods on two widely-used datasets.

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Zheng Tang, Gus Hahn-Powell, Mihai Surdeanu

SHIELD: LLM-Driven Schema Induction for Predictive Analytics in EV Battery Supply Chain Disruptions (2024.emnlp-industry)

Challenge:	EE is a key requirement for machine learning in many domains, e.g., legal, medical, finance.
Approach:	They propose an interpretable approach for event extraction that jointly trains a classifier and a rule decoder for event processing.
Outcome:	The proposed approach can be used for semi-supervised learning and its performance improves when trained on automatically-labeled data generated by a rule-based system.

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Zhi-Qi Cheng, Yifei Dong, Aike Shi, Wei Liu, Yuzhi Hu, Jason O’Connor, Alexander Hauptmann, Kate Whitefoot

LED: A Dataset for Life Event Extraction from Dialogs (2023.findings-eacl)

Challenge:	EV battery supply chain is vulnerable to disruptions caused by natural disasters and geopolitical tensions.
Approach:	They propose a system integrating Large Language Models with domain expertise for EV supply chain risk assessment.
Outcome:	Evaluated on 12,070 paragraphs from 365 sources (2022-2023), SHIELD outperforms baseline GCNs and LLM+prompt methods in disruption prediction.

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Yi-Pei Chen, An-Zi Yen, Hen-Hsen Huang, Hideki Nakayama, Hsin-Hsi Chen

Leveraging ChatGPT in Pharmacovigilance Event Extraction: An Empirical Study (2024.eacl-short)

Challenge:	Existing methods for extracting life events from conversations are limited.
Approach:	They propose a dataset containing fine-grained life event annotations on conversational data.
Outcome:	The proposed dataset combines three information extraction frameworks to extract life events from conversations.

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Zhaoyue Sun, Gabriele Pergola, Byron Wallace, Yulan He

Doc2EDAG: An End-to-End Document-level Framework for Chinese Financial Event Extraction (D19-1)

Challenge:	pharmacovigilance event extraction is a key field of healthcare that involves identifying, evaluating, understanding, and preventing adverse effects.
Approach:	They investigate the ability of large language models (LLMs) to extract adverse events from medical text.
Outcome:	The proposed model performs reasonably well with demonstration selection strategies, but falls short compared to fully fine-tuned small models.

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Shun Zheng, Wei Cao, Wei Xu, Jiang Bian

DocEE-zh: A Fine-grained Benchmark for Chinese Document-level Event Extraction (2024.findings-emnlp)

Challenge:	Existing event extraction methods are limited to extract event arguments within the sentence scope.
Approach:	They propose a model which generates an entity-based directed acyclic graph to fulfill document-level EE effectively.
Outcome:	The proposed model can generate entity-based directed acyclic graph to fulfill document-level EE effectively.

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Minghui Liu, MeiHan Tong, Yangda Peng, Lei Hou, Juanzi Li, Bin Xu

Joint Event and Temporal Relation Extraction with Shared Representations and Structured Prediction (D19-1)

Challenge:	Chinese document-level event extraction is still largely unexplored.
Approach:	They propose a Chinese document-level event extraction dataset with over 36,000 events and 210,000 arguments.
Outcome:	The proposed dataset includes over 36,000 events and more than 210,000 arguments . it is an extension of the DocEE dataset, utilizing the same event schema and annotated by human experts.

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Rujun Han, Qiang Ning, Nanyun Peng

CrudeOilNews: An Annotated Crude Oil News Corpus for Event Extraction (2022.lrec-1)

Challenge:	Existing systems treat this task as a pipeline of two separate subtasks, i.e., event extraction and temporal relation classification.
Approach:	They propose a joint event and temporal relation extraction model with shared representation learning and structured prediction.
Outcome:	The proposed method improves both event extraction and temporal relation extraction over state-of-the-art systems.

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Meisin Lee, Lay-Ki Soon, Eu Gene Siew, Ly Fie Sugianto

Event Extraction by Answering (Almost) Natural Questions (2020.emnlp-main)

Challenge:	a corpus of English crude oil news for event extraction is presented . the corpus contains 425 news articles with approximately 11k events annotated .
Approach:	They present a corpus of English Crude Oil news for event extraction . it is the first of its kind for Commodity News and contributes to text mining .
Outcome:	The proposed corpus of English crude oil news is the first of its kind for Commodity News . the annotated news articles are compared with the standard news articles .

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Xinya Du, Claire Cardie

Disease Network Constructor: a Pathway Extraction and Visualization (2023.acl-demo)

Challenge:	Existing work in event argument extraction relies heavily on entity recognition as a preprocessing/concurrent step, causing error propagation.
Approach:	They propose a question answering task that extracts event arguments in an end-to-end manner.
Outcome:	The proposed framework outperforms prior work on the ACE 2005 task on event argument extraction.

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Mohammad Golam Sohrab, Khoa Duong, Goran Topić, Masami Ikeda, Nozomi Nagano, Yayoi Natsume-Kitatani, Masakata Kuroda, Mari Itoh, Hiroya Takamura

Bridging the Gap between Native Text and Translated Text through Adversarial Learning: A Case Study on Cross-Lingual Event Extraction (2023.findings-eacl)

Challenge:	In the human body, various substances (entities) such as proteins and compounds interact and regulate each other, forming huge pathway networks.
Approach:	They present a system that extracts and visualizes a disease network derived through regulation events found in scientific articles on idiopathic pulmonary fibrosis.
Outcome:	The proposed system extracts and visualizes a disease network from biomedical articles on idiopathic pulmonary fibrosis (IPF) it includes two-dimensional (2D) and 3D visualizations of the constructed disease network.

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Pengfei Yu, Jonathan May, Heng Ji

Multilinguality

Challenge:	Recent research in cross-lingual learning has found that combining large-scale pretrained multilingual language models with machine translation can yield good performance.
Approach:	They propose a model architecture that jointly encodes a source language input sentence with its translation to the target language during training and takes a target language sentence with it as input during evaluation.
Outcome:	The proposed model architecture can integrate machine translation to improve event extraction while adding machine-translated data yields unstable performance due to representational gap.

Employing Glyphic Information for Chinese Event Extraction with Vision-Language Model (2024.findings-emnlp)

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Xiaoyi Bao, Jinghang Gu, Zhongqing Wang, Minjie Qiang, Chu-Ren Huang

DeepStruct: Pretraining of Language Models for Structure Prediction (2022.findings-acl)

Challenge:	Recent studies on event extraction have incorporated a variety of features, including textual elements and annotations.
Approach:	They propose a glyphic multi-modal Chinese event extraction model with hieroglyphic images to capture morphological structure from the sequence.
Outcome:	The proposed model can extract events from a Chinese and KBP Eval datasets at low cost.

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Chenguang Wang, Xiao Liu, Zui Chen, Haoyun Hong, Jie Tang, Dawn Song

Event Extraction as Multi-turn Question Answering (2020.findings-emnlp)

Challenge:	Pretrained language models perform structural understanding tasks that focus on understanding one aspect of the text.
Approach:	They propose a method for improving the structural understanding abilities of language models by pretraining them to generate structures from the text on task-agnostic corpora.
Outcome:	The proposed model performs state-of-the-art on 21 of 28 datasets.

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Fayuan Li, Weihua Peng, Yuguang Chen, Quan Wang, Lu Pan, Yajuan Lyu, Yong Zhu

PESE: Event Structure Extraction using Pointer Network based Encoder-Decoder Architecture (2022.aacl-main)

Challenge:	Current approaches to event extraction fail to model rich interactions among event types and arguments of different roles.
Approach:	They propose a new paradigm that formulates event extraction as multi-turn question answering . they propose to use reading comprehension problems to extract triggers and arguments .
Outcome:	The proposed approach outperforms current state-of-the-art on argument extraction tasks . it makes full use of dependency among arguments and event types, and generalizes well .

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Alapan Kuila, Sudeshna Sarkar

Extracting Trigger-sharing Events via an Event Matrix (2022.findings-emnlp)

Challenge:	Event extraction (EE) aims to find the events and event-related argument information from the text and represent them in a structured format.
Approach:	They propose to represent each event record in a unique tuple format that contains trigger phrase, trigger type, argument phrase, and corresponding role information.
Outcome:	The proposed model achieves competitive performance compared to the state-of-the-art methods.

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Jun Xu, Weidi Xu, Mengshu Sun, Taifeng Wang, Wei Chu

Event Semantic Classification in Context (2024.findings-eacl)

Challenge:	Existing methods to extract multiple events with triggers and arguments are invalid as there may be multiple events.
Approach:	They propose a framework for event extraction which models the relations between arguments by an event matrix.
Outcome:	The proposed framework beats all the advanced competitors on 3 widely-used datasets.

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Haoyu Wang, Hongming Zhang, Kaiqiang Song, Dong Yu, Dan Roth

Harvesting Events from Multiple Sources: Towards a Cross-Document Event Extraction Paradigm (2024.findings-acl)

Challenge:	In this work, we focus on the semantic classification of events in context to help machines gain a deeper understanding of events.
Approach:	They propose to integrate event semantics into downstream tasks to help machines understand events better.
Outcome:	The proposed model improves the understanding of events in context.

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Qiang Gao, Zixiang Meng, Bobo Li, Jun Zhou, Fei Li, Chong Teng, Donghong Ji

Document-Level Event Argument Extraction via Optimal Transport (2022.findings-acl)

Challenge:	Document-level event extraction aims to extract structured information from unstructured text.
Approach:	They propose a cross-document event extraction pipeline that integrates event information from multiple documents and provides a comprehensive perspective on events.
Outcome:	The proposed pipeline achieves about 72% F1 in end-to-end cross-document event extraction, setting up a benchmark for future research.

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Amir Pouran Ben Veyseh, Minh Van Nguyen, Franck Dernoncourt, Bonan Min, Thien Nguyen

DEGREE: A Data-Efficient Generation-Based Event Extraction Model (2022.naacl-main)

Challenge:	Prior work on event-level EAE models ignore syntactic structures for documents . prior work on EE is restricted to sentence-level setting where event triggers and arguments are assumed to appear in the same sentences.
Approach:	They propose to employ Optimal Transport to induce structures of documents based on sentence-level syntactic structures and tailored to EAE task.
Outcome:	The proposed model is effective in document-level EAE, with a new constraint on unrelated context words.

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I-Hung Hsu, Kuan-Hao Huang, Elizabeth Boschee, Scott Miller, Prem Natarajan, Kai-Wei Chang, Nanyun Peng

Responsible & Trustworthy NLP

Challenge:	Existing models for event extraction require expensive human annotations.
Approach:	They propose a data-efficient event extraction model that formulates event extraction as a conditional generation problem.
Outcome:	The proposed model can be trained with only a few labeled examples.

Everything Is All It Takes: A Multipronged Strategy for Zero-Shot Cross-Lingual Information Extraction (2021.emnlp-main)

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Mahsa Yarmohammadi, Shijie Wu, Marc Marone, Haoran Xu, Seth Ebner, Guanghui Qin, Yunmo Chen, Jialiang Guo, Craig Harman, Kenton Murray, Aaron Steven White, Mark Dredze, Benjamin Van Durme

Improving Event Definition Following For Zero-Shot Event Detection (2024.acl-long)

Challenge:	Zero-shot cross-lingual information extraction (IE) is a technique for training data in a source language but not in .
Approach:	They explore techniques including data projection and self-training to improve zero-shot cross-lingual information extraction (IE) IE is a construction of an IE model for some target language given existing annotations exclusively in English.
Outcome:	The proposed techniques show that they perform better than any single strategy.

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Zefan Cai, Po-Nien Kung, Ashima Suvarna, Mingyu Ma, Hritik Bansal, Baobao Chang, P. Jeffrey Brantingham, Wei Wang, Nanyun Peng

Probing into the Root: A Dataset for Reason Extraction of Structural Events from Financial Documents (2021.eacl-main)

Challenge:	Existing approaches on zero-shot event detection train models on datasets annotated with known event types and prompt them with unseen event definitions.
Approach:	They propose to train models to better follow event definitions by using an automatic generated Diverse Event Definition dataset.
Outcome:	The proposed model outperforms existing models on three open benchmarks on zero-shot event detection.

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Pei Chen, Kang Liu, Yubo Chen, Taifeng Wang, Jun Zhao

Global Constraints with Prompting for Zero-Shot Event Argument Classification (2023.findings-eacl)

Challenge:	Existing methods for event reason extraction are far from resolving this problem.
Approach:	They propose a task to extract causal explanations from document-level texts . they use a dataset FinReason for evaluation to provide Reasons annotation for financial events .
Outcome:	The proposed task performs better than existing methods on a dataset of 8,794 documents, 12,861 financial events and 11,006 reason spans.

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Zizheng Lin, Hongming Zhang, Yangqiu Song

Zero-Shot Transfer Learning for Event Extraction (P18-1)

Challenge:	Existing zero-shot trigger extraction models require annotations, which is not practical for open-domain applications.
Approach:	They propose to use global constraints with prompting to tackle event argument classification without annotation and task-specific training.
Outcome:	The proposed model outperforms the best zero-shot baselines by 12.5% and 10.9% F1 on ACE and ERE with given argument spans and by 4.3% and 3.3% F1 without given argument spas.

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Lifu Huang, Heng Ji, Kyunghyun Cho, Ido Dagan, Sebastian Riedel, Clare Voss

Multimedia Event Extraction with LLM Knowledge Editing (2025.emnlp-main)

Challenge:	Existing supervised event extraction methods rely on manual annotations and features specific to each event type.
Approach:	They propose a framework that maps event mentions to a specific type in an event ontology . they use existing annotations to extract event types from unstructured text data .
Outcome:	The proposed framework can be applied to new unseen event types without manual annotations.

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Jiaao Yu, Yijing Lin, Zhipeng Gao, Xuesong Qiu, Lanlan Rui

Event Extraction

Challenge:	Existing multimodal event extraction methods focus on weakly aligning features from wellpretrained unimodal encoders, resulting in redundant feature perception.
Approach:	They propose a multimodal event extraction strategy with a redundant feature selection mechanism that enhances event understanding ability of multimodal large language models.
Outcome:	The proposed method outperforms the state-of-the-art (SOTA) baselines on the M2E2 benchmark.

AD3: Attentive Deep Document Dater (D18-1)

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Swayambhu Nath Ray, Shib Sankar Dasgupta, Partha Talukdar

Towards Few-Shot Event Mention Retrieval: An Evaluation Framework and A Siamese Network Approach (2020.lrec-1)

Challenge:	Existing methods to predict creation time of documents are based on time-stamp metadata, but none are available.
Approach:	They propose an attention-based neural document dating system which utilizes both context and temporal information in documents in a flexible and principled manner.
Outcome:	The proposed system outperforms neural and non-neural baselines on multiple real-world datasets.

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Bonan Min, Yee Seng Chan, Lingjun Zhao

BKEE: Pioneering Event Extraction in the Vietnamese Language (2024.lrec-main)

Challenge:	Existing methods for event extraction are "one size fits all" and are not adaptable to new event types or domains of interest.
Approach:	They propose a few-shot Event Mention Retrieval task to retrieve event mentions from text . they use existing event datasets such as ACE and a Siamese Network approach .
Outcome:	The proposed approach lowers the bar of specifying event-centric information needs.

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Thi-Nhung Nguyen, Bang Tien Tran, Trong-Nghia Luu, Thien Huu Nguyen, Kiem-Hieu Nguyen

UTC-IE: A Unified Token-pair Classification Architecture for Information Extraction (2023.acl-long)

Challenge:	Event Extraction (EE) is a fundamental task in information extraction.
Approach:	They propose a Vietnamese event extraction dataset that includes 33 different event types and 28 different event argument roles.
Outcome:	The proposed dataset provides a labeled dataset for entity mentions, event mentions and event arguments on 1066 documents.

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Hang Yan, Yu Sun, Xiaonan Li, Yunhua Zhou, Xuanjing Huang, Xipeng Qiu

How Does Context Matter? On the Robustness of Event Detection with Context-Selective Mask Generalization (2020.findings-emnlp)

Challenge:	Information Extraction (IE) tasks have been solved with different models because of their output structures.
Approach:	They propose a Unified Token-pair Classification architecture for Information Extraction that introduces Plusformer on top of the token-pear feature matrix.
Outcome:	The proposed approach outperforms task-specific and unified models on all tasks in 10 datasets and achieves better results on 2 joint IE datasets.

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Jian Liu, Yubo Chen, Kang Liu, Yantao Jia, Zhicheng Sheng

Robustness in NLP

Challenge:	Existing studies focus on improving the overall performance of an ED model, but few consider the robustness of an existing model.
Approach:	They propose a new training mechanism that can effectively mine context-specific patterns for learning and robustify an ED model.
Outcome:	The proposed model can learn a complementary predictive bias with most ED models that use full context for feature learning.

Text-to-Text Extraction and Verbalization of Biomedical Event Graphs (2022.coling-1)

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Giacomo Frisoni, Gianluca Moro, Lorenzo Balzani

Joint Event Extraction with Hierarchical Policy Network (2020.coling-main)

Challenge:	Biomedical events represent complex, graphical, and semantically rich interactions expressed in the scientific literature.
Approach:	They propose a framework to solve event extraction and event verbalization with a unified text-to-text approach.
Outcome:	The proposed framework achieves greater state-of-the-art performance than single-task competitors and can generate coherent natural language utterances from structured data.

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Peixin Huang, Xiang Zhao, Ryuichi Takanobu, Zhen Tan, Weidong Xiao

Cross-lingual Structure Transfer for Zero-resource Event Extraction (2020.lrec-1)

Challenge:	Existing work on event extraction (EE) is pipelined or uses a joint structure but does not utilize information interactions among event triggers, event arguments, and argument roles.
Approach:	They propose to exploit role information of arguments in an event and devise a Hierarchical Policy Network to perform joint EE.
Outcome:	The proposed system outperforms existing methods and is more powerful for sentences with multiple events.

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Di Lu, Ananya Subburathinam, Heng Ji, Jonathan May, Shih-Fu Chang, Avi Sil, Clare Voss

ENPAR:Enhancing Entity and Entity Pair Representations for Joint Entity Relation Extraction (2021.eacl-main)

Challenge:	Existing approaches for information extraction only use name tagging . Currently, most successful cross-lingual transfer learning methods are limited to sequence labeling .
Approach:	They propose a share-and-transfer framework to transfer graph structures across languages . they propose to convert sentences in any language to language-universal graph structures .
Outcome:	The proposed framework performs comparable to state-of-the-art models on three languages without annotations.

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Yijun Wang, Changzhi Sun, Yuanbin Wu, Hao Zhou, Lei Li, Junchi Yan

Explicit Role Interaction Network for Event Argument Extraction (2022.findings-emnlp)

Challenge:	Existing methods for joint entity relation extraction use multitask learning frameworks, but annotations for additional tasks are hard to obtain.
Approach:	They propose a pre-training method to improve the joint extraction performance with just extra entity annotations.
Outcome:	The proposed method outperforms existing methods on ACE05, SciERC, and NYT and outperformed BERT on other tasks.

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Nan Ding, Chunming Hu, Kai Sun, Samuel Mensah, Richong Zhang

OEE-CFC: A Dataset for Open Event Extraction from Chinese Financial Commentary (2024.findings-emnlp)

Challenge:	Existing methods extract arguments of each role independently, ignoring the relationship between different roles.
Approach:	They propose a neural model that captures the correlations between different argument roles within an event.
Outcome:	Extensive experiments on the benchmark dataset ACE2005 show the superiority of the proposed model over existing methods.

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Qizhi Wan, Changxuan Wan, Rong Hu, Dexi Liu, Xu Wenwu, Kang Xu, Zou Meihua, Liu Tao, Jie Yang, Zhenwei Xiong

Event Extraction from Unstructured Amharic Text (2020.lrec-1)

Challenge:	Existing corpora with unconventional entities serving as event arguments lack rich multi-events and shared arguments.
Approach:	They develop an open event template that includes 21 event argument roles and an open corpus supporting open event extraction.
Outcome:	The proposed corpus includes 17,469 events, 44,221 arguments, 3,644 complex arguments, and 5,898 shared arguments.

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Ephrem Tadesse, Rosa Tsegaye, Kuulaa Qaqqabaa

Contextualized Soft Prompts for Extraction of Event Arguments (2023.findings-acl)

Challenge:	Existing information extraction systems for Amharic have not represented the linguistic structure and morphological richness of the languages.
Approach:	They propose a system that extracts an event from unstructured Amharic text using supervised machine learning and rule-based approaches.
Outcome:	The proposed system outperforms the existing rule-based method on Amharic text.

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Chien Nguyen, Hieu Man, Thien Nguyen

EXCEEDS: Extracting Complex Events via Nugget-based Grid Modeling in Scientific Domain (2026.acl-long)

Challenge:	Existing prompt-based methods for event argument extraction rely on discrete and manually-designed prompts that cannot exploit specific context for each example.
Approach:	They propose a prompt-based method that introduces soft prompts to facilitate encoding of individual example context and multiple relevant documents to boost EAE.
Outcome:	The proposed method extensively evaluates on benchmark datasets to demonstrate its benefits with state-of-the-art performance.

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Yi-Fan Lu, Xian-Ling Mao, Bo Wang, Xiao Liu, Heyan Huang

Towards Better Multi-task Learning: A Framework for Optimizing Dataset Combinations in Large Language Models (2025.findings-naacl)

Challenge:	Extensive event extraction research has been conducted in many domains, including news, finance, and biology.
Approach:	They propose an end-to-end scientific event extraction framework for encoding nuggets into a grid matrix and simplifying complex event extraction as a nuggot-based grid modeling task.
Outcome:	The proposed framework performs well in scientific domain, demonstrating state-of-the-art performance.

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Zaifu Zhan, Rui Zhang

Responsible & Trustworthy NLP

Challenge:	Using a neural network, large language models can be trained on multiple tasks, allowing them to perform tasks efficiently.
Approach:	They propose a framework that leverages a neural network to select the best dataset combinations for enhancing multi-task learning (MTL) They propose to iteratively refine the selection, greatly improving efficiency while being model-, dataset-, and domain-independent.
Outcome:	The proposed framework iteratively refines the selection, greatly improving efficiency, while being model-, dataset-, and domain-independent.

CMNEE:A Large-Scale Document-Level Event Extraction Dataset Based on Open-Source Chinese Military News (2024.lrec-main)

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Mengna Zhu, Zijie Xu, Kaisheng Zeng, Kaiming Xiao, Mao Wang, Wenjun Ke, Hongbin Huang

CollabKG: A Learnable Human-Machine-Cooperative Information Extraction Toolkit for (Event) Knowledge Graph Construction (2024.lrec-main)

Challenge:	Current research focuses on the general news or financial domains, with relatively few studies for military domain.
Approach:	They propose to annotate Chinese military news events from documents using a schema for the military domain.
Outcome:	The proposed dataset is large-scale, document-level open-source for the military domain . it contains 17,000 documents and 29,223 events, which are all manually annotated .

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Xiang Wei, Yufeng Chen, Ning Cheng, Xingyu Cui, Jinan Xu, Wenjuan Han

Saliency as Evidence: Event Detection with Trigger Saliency Attribution (2022.acl-long)

Challenge:	Existing IE tools lack multi-task support and automatic updates for KG and EKG construction.
Approach:	They propose a human-machine-cooperative IE toolkit for KG and EKG construction that unifies different IE subtasks and integrates LLMs as the assistant machine.
Outcome:	The proposed tool improves annotation quality, efficiency, and stability simultaneously.

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Jian Liu, Yufeng Chen, Jinan Xu

Mask-then-Fill: A Flexible and Effective Data Augmentation Framework for Event Extraction (2022.findings-emnlp)

Challenge:	Existing methods to ED see no differences between event types and use a single model to address them all.
Approach:	They propose a new concept termed trigger salience attribution which can explicitly quantify the underlying patterns of events.
Outcome:	The proposed model can distinguish between trigger-dependent and context-dependent types and achieve promising performance on two benchmarks.

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Jun Gao, Changlong Yu, Wei Wang, Huan Zhao, Ruifeng Xu

Dynamic Prefix-Tuning for Generative Template-based Event Extraction (2022.acl-long)

Challenge:	Existing data augmentation methods for event extraction are costly and time-consuming.
Approach:	They propose a data augmentation framework that randomly masks out an adjunct sentence fragment and infills a variable-length text span with a fine-tuned infilling model.
Outcome:	The proposed framework can generate more diverse data while keeping the original structure unchanged . it can replace a fragment of arbitrary length in the text with another fragment of variable length .

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Xiao Liu, Heyan Huang, Ge Shi, Bo Wang

Construct a Sense-Frame Aligned Predicate Lexicon for Chinese AMR Corpus (2020.lrec-1)

Challenge:	Experimental results show that our model achieves competitive results with the state-of-the-art classification-based model OneIE on ACE 2005.
Approach:	They propose a generative template-based event extraction method with dynamic prefix . they integrate context information with type-specific prefixes to learn a context-specific name for each context .
Outcome:	The proposed method achieves competitive results with state-of-the-art model OneIE on ACE 2005 and performs well on ERE.

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Li Song, Yuling Dai, Yihuan Liu, Bin Li, Weiguang Qu

RAAT: Relation-Augmented Attention Transformer for Relation Modeling in Document-Level Event Extraction (2022.naacl-main)

Challenge:	Existing lexicons blur senses and frames of predicates, which needs to be refined to meet word sense disambiguation and event extraction tasks.
Approach:	They propose to construct a predicate lexicon for Chinese AMR corpus with 14,389 senses and 10,800 frames of 8,470 words.
Outcome:	The proposed lexicon includes 14,389 senses and 10,800 frames of 8,470 words.

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Yuan Liang, Zhuoxuan Jiang, Di Yin, Bo Ren

Open-Vocabulary Argument Role Prediction For Event Extraction (2022.findings-emnlp)

Challenge:	Existing methods focus on sentencelevel event extraction (SEE), but they are inconsistent with actual situations.
Approach:	They propose a document-level event extraction framework which can model relation dependencies by a relation-augmented Attention Transformer.
Outcome:	The proposed framework can achieve state-of-the-art performance on two public datasets.

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Yizhu Jiao, Sha Li, Yiqing Xie, Ming Zhong, Heng Ji, Jiawei Han

EFSA: Towards Event-Level Financial Sentiment Analysis (2024.acl-long)

Challenge:	Existing studies on event extraction depend on pre-defined argument roles . despite great progress, many studies still rely on hand-crafted ontologies .
Approach:	They propose an unsupervised framework for customizing argument roles for event extraction . they propose a human-annotated event extraction dataset with 143 customized argument roles .
Outcome:	The proposed framework outperforms existing methods on an event extraction dataset.

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Tianyu Chen, Yiming Zhang, Guoxin Yu, Dapeng Zhang, Li Zeng, Qing He, Xiang Ao

Sentiment Analysis

Challenge:	a large-scale Chinese dataset contains 12,160 news articles and 13,725 quintuples . a four-hop Chain-of-Thought LLM-based approach is devised for this task .
Approach:	They propose to extend financial sentiment analysis to event-level since events usually serve as the subject of the sentiment in financial text.
Outcome:	The proposed method can reach the current state-of-the-art on a large-scale Chinese dataset.

Revisiting Classical Chinese Event Extraction with Ancient Literature Information (2025.acl-long)

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Xiaoyi Bao, Zhongqing Wang, Jinghang Gu, Chu-Ren Huang

Adaptive Schema-aware Event Extraction with Retrieval-Augmented Generation (2025.findings-emnlp)

Challenge:	Existing studies on classical Chinese event extraction focus on grafting the complex modeling from English or modern Chinese works, neglecting the unique characteristic of this language.
Approach:	They propose a Literary Vision-Language Model (VLM) for classical Chinese event extraction . they integrate annotations, historical background and character glyphs to capture the inner- and outer-context information from the sequence.
Outcome:	The proposed model can capture the inner- and outer-context information at nearly zero cost.

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Sheng Liang, Hang Lv, Zhihao Wen, Yaxiong Wu, Yongyue Zhang, Hao Wang, Yong Liu

Title2Event: Benchmarking Open Event Extraction with a Large-scale Chinese Title Dataset (2022.emnlp-main)

Challenge:	Event extraction is a task in natural language processing that involves identifying and extracting event information from unstructured text.
Approach:	They propose a paradigm that combines schema paraphrasing with schema retrieval-augmented generation.
Outcome:	The proposed paradigm retrieves paraphrased schemas and accurately generates targeted structures.

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Haolin Deng, Yanan Zhang, Yangfan Zhang, Wangyang Ying, Changlong Yu, Jun Gao, Wei Wang, Xiaoling Bai, Nan Yang, Jin Ma, Xiang Chen, Tianhua Zhou

Soft Syntactic Reinforcement for Neural Event Extraction (2025.naacl-long)

Challenge:	Existing EE datasets define fixed event types and design specific schemas for each of them, failing to cover diverse events emerging from the online text.
Approach:	They propose to use a sentence-level dataset to benchmark Open Event Extraction without restricting event types.
Outcome:	The proposed dataset contains more than 42,000 news titles in 34 topics collected from Chinese web pages.

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Anran Hao, Jian Su, Shuo Sun, Teo Yong Sen

Syntactic Text Processing

Challenge:	Recent event extraction methods rely on pre-trained language models but still suffer from errors due to a lack of syntactic knowledge.
Approach:	They propose a method to incorporate syntactic information into PLM-based models for event extraction (EE) this method uses a standard dependency corpus to select syntax-related dimensions of the model's representation.
Outcome:	The proposed method outperforms baseline models and existing syntactic reinforcement methods on sentence-level and document-level EE benchmark datasets.

Emancipating Event Extraction from the Constraints of Long-Tailed Distribution Data Utilizing Large Language Models (2024.lrec-main)

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Zhigang Kan, Liwen Peng, Linbo Qiao, Dongsheng Li

A Dataset for Multi-lingual Epidemiological Event Extraction (2020.lrec-1)

Challenge:	Existing methods for EE depend on manual annotations, which are expensive and scarce.
Approach:	They propose to transform the event extraction task into multi-turn dialogues and a novel method for generating high-quality data.
Outcome:	The proposed methods significantly improve existing models’ performance with various paradigms and structures, especially on tail types.

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Stephen Mutuvi, Antoine Doucet, Gaël Lejeune, Moses Odeo

Exploring Pre-trained Language Models for Event Extraction and Generation (P19-1)

Challenge:	Using the Web, we propose a corpus for information extraction and text classification.
Approach:	They propose to use a corpus for information extraction and natural language processing (NLP) tasks such as text classification.
Outcome:	The proposed corpus can be used for information extraction and natural language processing tasks such as text classification.

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Sen Yang, Dawei Feng, Linbo Qiao, Zhigang Kan, Dongsheng Li

Fact vs. Opinion: the Role of Argumentation Features in News Classification (2020.coling-main)

Challenge:	Existing methods to extract event data are laborious to create and limited in size.
Approach:	They propose an event extraction model to overcome the roles overlap problem by separating the argument prediction in terms of roles.
Outcome:	The proposed method surpasses existing methods on the ACE2005 dataset and improves on the previous methods.

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Tariq Alhindi, Smaranda Muresan, Daniel Preotiuc-Pietro

Text Classification

Challenge:	A 2018 study led by the Media Insight Project showed that most journalists think that their news organizations should clearly mark what is news reporting and what is commentary or opinion in order to combat fake news and gain public trust.
Approach:	They propose to classify news articles into newsstories and opinion pieces using models that aim to sup-plement the article content representation with argumentation features.
Outcome:	The proposed model outperforms linguistic features and improves on fine-tuned transformer-based models on data from publishers.

Verb Knowledge Injection for Multilingual Event Processing (2021.acl-long)

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Olga Majewska, Ivan Vulić, Goran Glavaš, Edoardo Maria Ponti, Anna Korhonen

Evaluating Generative Language Models in Information Extraction as Subjective Question Correction (2024.lrec-main)

Challenge:	Recent studies have shown that pretrainers implicitly extract a non-negligible amount of linguistic knowledge from text corpora in an unsupervised fashion.
Approach:	They propose to inject explicit verb knowledge into dedicated adapter modules to complement the linguistic knowledge obtained during LM-pretraining.
Outcome:	The proposed model improves in English event extraction tasks, while injecting verb knowledge improves other languages.

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Yuchen Fan, Yantao Liu, Zijun Yao, Jifan Yu, Lei Hou, Juanzi Li

Multilingual Event Extraction from Historical Newspaper Adverts (2023.acl-long)

Challenge:	Modern large language models (LLMs) perform poorly in elementary tasks like relation extraction and event extraction due to two issues in conventional evaluation methods.
Approach:	They propose a method to evaluate large language models by incorporating a human annotation schema.
Outcome:	The proposed evaluation method improves matching between model outputs and golden labels.

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Nadav Borenstein, Natália da Silva Perez, Isabelle Augenstein

Multilinguality

Challenge:	Developing NLP methods for historical corpora is difficult, as only domain experts can label them . off-the-shelf models are trained on modern language texts, rendering them weaker for historical documents .
Approach:	They propose to use an annotated newspaper dataset to extract historical data from a novel domain of texts.
Outcome:	The proposed method performs well on a multilingual dataset in English, French, and Dutch . it is possible to extract surprisingly good results even with scarce annotated data using existing models and datasets for modern languages .

Reporting the Unreported: Event Extraction for Analyzing the Local Representation of Hate Crimes (D19-1)

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Aida Mostafazadeh Davani, Leigh Yeh, Mohammad Atari, Brendan Kennedy, Gwenyth Portillo Wightman, Elaine Gonzalez, Natalie Delong, Rhea Bhatia, Arineh Mirinjian, Xiang Ren, Morteza Dehghani

Entity, Relation, and Event Extraction with Contextualized Span Representations (D19-1)

Challenge:	Existing estimates of hate crimes in the US are under-reported relative to actual number of incidents.
Approach:	They propose to use event extraction and multi-instance learning to predict hate crimes in local news articles for cities without official FBI reports.
Outcome:	The proposed model compares to FBI reports and shows that hate crimes are under-reported in local press.

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David Wadden, Ulme Wennberg, Yi Luan, Hannaneh Hajishirzi

Extracting Space Situational Awareness Events from News Text (2022.lrec-1)

Challenge:	Existing frameworks for named entity recognition, relation extraction, and event extraction can be easily adapted for new tasks or datasets.
Approach:	They propose a framework that enumerates, refins, and scores text spans to capture local (within-sentence) and global (cross-sentent) context.
Outcome:	The proposed framework achieves state-of-the-art results on four datasets from a variety of domains.

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Zhengnan Xie, Alice Saebom Kwak, Enfa George, Laura W. Dozal, Hoang Van, Moriba Jah, Roberto Furfaro, Peter Jansen

Learning from a Friend: Improving Event Extraction via Self-Training with Feedback from Abstract Meaning Representation (2023.findings-acl)

Challenge:	Space situational awareness is the decisionmaking knowledge required to predict, avoid, operate through, or recover from the loss, disruption, or degradation of space services, capabilities, or activities.
Approach:	They construct a corpus of 48.5k news articles spanning all known active satellites between 2009 and 2020 that are annotated by humans with 15.9k labels for event slots.
Outcome:	The proposed system achieves an overall F1 between 53 and 91 per slot for event extraction in this low-resource, high-impact domain.

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Zhiyang Xu, Jay Yoon Lee, Lifu Huang

Explicit, Implicit, and Scattered: Revisiting Event Extraction to Capture Complex Arguments (2024.emnlp-main)

Challenge:	Existing data scarcity hinders the progress of event extraction, authors say . ACE-052 has 10 of the 33 event types with less than 80 annotations, authors claim .
Approach:	They propose a self-training with feedback framework that leverages large-scale unlabeled data to acquire feedback for each new event prediction from the unlabed data.
Outcome:	The proposed framework improves event extraction models even when unlabeled data are unavailable.

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Omar Sharif, Joseph Gatto, Madhusudan Basak, Sarah Masud Preum

Instruction-Tuning LLMs for Event Extraction with Annotation Guidelines (2025.findings-acl)

Challenge:	Existing work on event-specific argument extraction is limited to contiguous spans of text . Existing approaches to event-centric information extraction are limited to explicit arguments .
Approach:	They propose two key argument types that cannot be modeled by existing EE frameworks . implicit and scattered arguments are crucial to elicit full breadth of information required for proper event modeling.
Outcome:	The proposed dataset includes 7,464 argument annotations from online health discourse.

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Saurabh Srivastava, Sweta Pati, Ziyu Yao

Revisiting Event Argument Extraction: Can EAE Models Learn Better When Being Aware of Event Co-occurrences? (2023.acl-long)

Challenge:	Existing applications of large language models to IE can be categorized into two lines: prompt engineering-based approaches and instruction-tuning open-weight LLMs.
Approach:	They propose to use annotation guidelines to teach large language models for event extraction . they use textual descriptions of event types and arguments to train the models .
Outcome:	The proposed approach improves cross-schema generalization and low-frequency event-type performance when there is a decent amount of training data.

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Yuxin He, Jingyue Hu, Buzhou Tang

Document-Level Event Role Filler Extraction using Multi-Granularity Contextualized Encoding (2020.acl-main)

Challenge:	Recent studies on event argument extraction (EAE) have not taken event co-occurrences into account.
Approach:	They propose to reformulate event co-occurrences as a problem of table generation and extend a SOTA prompt-based EAE model into a non-autoregressive generation framework that extracts the arguments of multiple events in parallel.
Outcome:	The proposed framework can extract arguments of multiple events in parallel.

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Xinya Du, Claire Cardie

Boosting Event Extraction with Denoised Structure-to-Text Augmentation (2023.findings-acl)

Challenge:	Document-level event extraction requires a view of a larger context to determine which spans of text correspond to event role fillers.
Approach:	They propose a multi-granularity reader to dynamically aggregate information captured by neural representations learned at different levels of granularities.
Outcome:	The proposed model performs substantially better than previous models on the MUC-4 event extraction dataset.

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Bo Wang, Heyan Huang, Xiaochi Wei, Ge Shi, Xiao Liu, Chong Feng, Tong Zhou, Shuaiqiang Wang, Dawei Yin

TextEE: Benchmark, Reevaluation, Reflections, and Future Challenges in Event Extraction (2024.findings-acl)

Challenge:	Existing methods for event extraction neglect grammatical incorrectness, structure misalignment, and semantic drifting . et al., 2004; Ahn, 2006) show that the proposed method generates more diverse text representations for event extracting compared with the state-of-the-art.
Approach:	They propose a framework for event extraction that generates additional training data and iteratively selects the effective subset from the generated training data.
Outcome:	The proposed method generates more diverse representations of training data and achieves comparable results with the state-of-the-art.

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Kuan-Hao Huang, I-Hung Hsu, Tanmay Parekh, Zhiyu Xie, Zixuan Zhang, Prem Natarajan, Kai-Wei Chang, Nanyun Peng, Heng Ji

Information Extraction with Differentiable Beam Search on Graph RNNs (2024.lrec-main)

Challenge:	Recent studies suggest that event extraction evaluations may not accurately reflect the true performance.
Approach:	They propose a standardized, fair, and reproducible benchmark for event extraction . they use standardized scripts and splits for 16 datasets spanning eight domains .
Outcome:	The proposed benchmarks show that they struggle to achieve satisfactory performance.

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Niama El Khbir, Nadi Tomeh, Thierry Charnois

Structured Data in NLP

Challenge:	Existing approaches to information extraction suffer from exposure bias due to discrepancy between training and decoding.
Approach:	They propose to cast graph generation as auto-regressive sequence labeling and make it aware of decoding procedure by using differentiable beam search.
Outcome:	The proposed model outperforms its non-decoding-aware version on ACE05 and ConLL04 datasets.

MailEx: Email Event and Argument Extraction (2023.emnlp-main)

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Saurabh Srivastava, Gaurav Singh, Shou Matsumoto, Ali Raz, Paulo Costa, Joshua Poore, Ziyu Yao

Adaptive Ordered Information Extraction with Deep Reinforcement Learning (2023.findings-acl)

Challenge:	Existing work on email event extraction only covers one specific aspect of email information and cannot connect with other relevant tasks.
Approach:	They propose a new taxonomy for performing event extraction from conversational email threads.
Outcome:	The proposed taxonomy covers 10 event types and 76 arguments in the email domain.

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Wenhao Huang, Jiaqing Liang, Zhixu Li, Yanghua Xiao, Chuanjun Ji

Leveraging Information Redundancy of Real-World Data through Distant Supervision (2024.lrec-main)

Challenge:	Existing methods for information extraction follow a fixed extraction order for complex tasks with multiple elements to be extracted in one instance.
Approach:	They propose an adaptive ordered IE paradigm to find optimal element extraction order for different instances and a reinforcement learning framework to generate optimal order dynamically.
Outcome:	The proposed method beats existing methods and improves on several public datasets.

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Ariel Cohen, Alexandrine Lanson, Emmanuelle Kempf, Xavier Tannier

Challenge:	Existing methods for annotation of health care notes are promising but they are limited due to privacy regulations.
Approach:	They propose a text labeling method that leverages the redundancy of temporal information in a data lake to create a large programmatically annotated corpus and train transformer models using distant supervision.
Outcome:	The proposed method reduces expert annotation time, a scarce and expensive resource.

UniEX: An Effective and Efficient Framework for Unified Information Extraction via a Span-extractive Perspective (2023.acl-long)

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Yang Ping, JunYu Lu, Ruyi Gan, Junjie Wang, Yuxiang Zhang, Pingjian Zhang, Jiaxing Zhang

Guideline Learning for In-Context Information Extraction (2023.emnlp-main)

Challenge:	Existing approaches for information extraction (IE) are limited by the number of subtasks and the isolation of the subtask.
Approach:	They propose a new paradigm for universal information extraction that is compatible with any schema format and applicable to a list of IE tasks.
Outcome:	The proposed framework outperforms generative universal IE models on 14 benchmarks with the supervised setting and the state-of-the-art performance in low-resource scenarios.

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Chaoxu Pang, Yixuan Cao, Qiang Ding, Ping Luo

Media Attitude Detection via Framing Analysis with Events and their Relations (2024.emnlp-main)

Challenge:	Large language models can perform a task by conditioning on task instructions and a few input-output examples without optimizing any parameters.
Approach:	They propose a guideline learning framework for In-context IE which reflectively learns and follows guidelines.
Outcome:	The proposed framework improves the performance of in-context IE by synthesizing and following guidelines.

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Jin Zhao, Jingxuan Tu, Han Du, Nianwen Xue

Challenge:	a recent study examined the effects of media framing on public perception and understanding of news articles.
Approach:	They propose to extract framing devices employed by media to assess their role in framating the narrative.
Outcome:	The proposed method surpasses baseline models and offers a more detailed and explainable analysis of media framing effects.

Debate as Optimization: Adaptive Conformal Prediction and Diverse Retrieval for Event Extraction (2024.findings-emnlp)

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Sijia Wang, Lifu Huang

Schema-based Data Augmentation for Event Extraction (2024.lrec-main)

Challenge:	Experimental results show a significant performance gap between tuning-based approaches and event extraction approaches.
Approach:	They propose a debate as optimization system where the primary objective is to iteratively refine the large language models outputs through debating without parameter tuning.
Outcome:	The proposed system reduces performance gap between supervised approaches and tuning-free methods by 18.1% and 17.8% on ACE05 and 17.9% and 15.2% on CASIE respectively.

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Xiaomeng Jin, Heng Ji