Papers with Event extraction
Hierarchical Chinese Legal event extraction via Pedal Attention Mechanism (2020.coling-main)
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| Challenge: | Existing methods for event extraction cannot express connections between arguments, which are crucial in legal events. |
| Approach: | They propose a dynamic event structure for Chinese legal events to distinguish between similar events by hierarchical event features for event detection and a pedal attention mechanism to extract the semantic relation between two words through their dependent adjacent words. |
| Outcome: | The proposed model surpasses state-of-the-art models on a Chinese legal event dataset. |
CasEE: A Joint Learning Framework with Cascade Decoding for Overlapping Event Extraction (2021.findings-acl)
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| 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 . |
Query and Extract: Refining Event Extraction as Type-oriented Binary Decoding (2022.findings-acl)
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| Challenge: | Existing approaches to event extraction are limited to a set of pre-defined types. |
| Approach: | They propose a natural language query framework that uses event types and argument roles to extract candidate triggers and arguments from input text. |
| Outcome: | The proposed framework outperforms existing methods on zero-shot event extraction. |
DocEE-zh: A Fine-grained Benchmark for Chinese Document-level Event Extraction (2024.findings-emnlp)
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| 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. |
Zero-shot Event Extraction via Transfer Learning: Challenges and Insights (2021.acl-short)
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| Challenge: | Existing methods for event extraction require expensive annotation and are not extensible to new event ontologies. |
| Approach: | They propose to use textual entailment and/or question answering queries to extract a zero-shot event from a set of TE and/ or QA queries. |
| Outcome: | The proposed method achieves acceptable results on ACE-2005 and ERE, but there is still a large gap from supervised approaches. |
Document-Level Event Argument Extraction by Conditional Generation (2021.naacl-main)
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| Challenge: | Existing event extraction models have been limited to the sentence level . this formulation signifies a misalignment between the information seeking behavior and the informative seeking behavior. |
| Approach: | They propose a document-level neural event argument extraction model by formulating the task as conditional generation following event templates. |
| Outcome: | The proposed model achieves 7.6% F1 and 5.7% F1 over the best baseline on the document-level event extraction dataset WikiEvents and 9.3% F1 on the informative argument extraction task. |
Event Extraction as Multi-turn Question Answering (2020.findings-emnlp)
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| 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 . |
Event Extraction as Machine Reading Comprehension (2020.emnlp-main)
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| Challenge: | Event extraction (EE) is a crucial information extraction task that aims to extract event information in texts. |
| Approach: | They propose a new learning paradigm for event extraction by explicitly casting it as a machine reading comprehension problem. |
| Outcome: | The proposed model achieves state-of-the-art performance on the data-scarce scenario, achieving 49.8% in F1 for event argument extraction with only 1% data, compared with 2.2% of the previous method. |
DEGREE: A Data-Efficient Generation-Based Event Extraction Model (2022.naacl-main)
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I-Hung Hsu, Kuan-Hao Huang, Elizabeth Boschee, Scott Miller, Prem Natarajan, Kai-Wei Chang, Nanyun Peng
| 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. |
Biomedical Event Extraction based on Knowledge-driven Tree-LSTM (N19-1)
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| Challenge: | Biomedical event extraction requires domain-specific knowledge and deep understanding of complex contexts. |
| Approach: | They propose a knowledge base-driven tree-structured long short-term memory networks framework . tree-LSTM framework incorporates dependency structures and entity properties from ontologies . |
| Outcome: | The proposed framework is based on the BioNLP shared task with Genia dataset and achieves state-of-the-art results. |
Jointly Multiple Events Extraction via Attention-based Graph Information Aggregation (D18-1)
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| Challenge: | Event extraction is of practical utility in natural language processing . it is common that multiple events exist in the same sentence, causing difficulties in extracting them . |
| Approach: | They propose a framework to jointly extract multiple event triggers and arguments . they introduce syntactic shortcut arcs to enhance information flow and attention-based graph convolution networks to model graph information. |
| Outcome: | The proposed framework achieves competitive results compared with state-of-the-art methods. |
OneEE: A One-Stage Framework for Fast Overlapping and Nested Event Extraction (2022.coling-1)
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| Challenge: | Event extraction (EE) is an essential task of information extraction, which aims to extract structured event information from unstructured text. |
| Approach: | They propose a tagging scheme and a model to form EE as word-word relation recognition using parallel grid tapping. |
| Outcome: | The proposed model achieves state-of-the-art on 3 overlapped and nested EE benchmarks and faster than baselines. |
Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extraction (2021.acl-long)
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| Challenge: | Existing methods to extract event records from text decompose complex structure prediction task into multiple subtasks. |
| Approach: | They propose a sequence-to-structure generation paradigm that can extract events from text . they propose unified event extraction, constrained decoding algorithm and curriculum learning algorithm . |
| Outcome: | The proposed method can achieve competitive performance using record-level annotations in both supervised learning and transfer learning settings. |
Event Extraction from Unstructured Amharic Text (2020.lrec-1)
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| 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. |
DocEE: A Large-Scale and Fine-grained Benchmark for Document-level Event Extraction (2022.naacl-main)
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| Challenge: | Existing datasets focus on sentence-level event extraction, but document-level EE is limited due to the lack of large-scale and practical training and evaluation datasets. |
| Approach: | They propose a document-level event extraction dataset with 27,000+ events and 180,000+ arguments. |
| Outcome: | The proposed dataset includes 27,000+ events, 180,000+ arguments and large-scale manual annotations, fine-grained argument types and application-oriented settings. |
Revisiting the Evaluation of End-to-end Event Extraction (2021.findings-acl)
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| Challenge: | Existing EE research uses the role-averaged evaluation metric, but it is misleading to downstream applications. |
| Approach: | They propose two new evaluation metrics that explicitly penalize wrongly identified event arguments. |
| Outcome: | The proposed evaluation metrics improve the initial evaluation by 10% . the proposed training scheme is better than the existing one, the authors show . |
Adaptive Schema-aware Event Extraction with Retrieval-Augmented Generation (2025.findings-emnlp)
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| 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. |
Title2Event: Benchmarking Open Event Extraction with a Large-scale Chinese Title Dataset (2022.emnlp-main)
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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
| 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. |
CLEVE: Contrastive Pre-training for Event Extraction (2021.acl-long)
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| Challenge: | Existing EE methods do not model event characteristics from large unsupervised data. |
| Approach: | They propose a contrastive pre-training framework for event extraction to better learn event knowledge from large unsupervised data and their semantic structures. |
| Outcome: | The proposed framework improves on ACE 2005 and MAVEN datasets on event extraction tasks. |
Efficient Zero-shot Event Extraction with Context-Definition Alignment (2022.findings-emnlp)
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| Challenge: | Conventional supervised methods cannot generalize to event types out of the pre-defined ontology. |
| Approach: | They propose to use two separate transformer models to model the definition semantics of an event type name into the same embedding space and then minimize their embeddable distance via contrastive learning. |
| Outcome: | The proposed model outperforms all previous zero-shot EE methods with fast inference speed due to the disjoint design. |
The Devil is in the Details: On the Pitfalls of Event Extraction Evaluation (2023.findings-acl)
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| Challenge: | Event extraction (EE) is a fundamental information extraction task aimed at extracting events from plain texts. |
| Approach: | They propose to specify data preprocessing, standardize outputs, and provide pipeline evaluation results to avoid these pitfalls. |
| Outcome: | The results show that the evaluations are reliable and lack pipeline evaluations. |
Event Extraction in Video Transcripts (2022.coling-1)
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| Challenge: | Existing EE datasets are limited to formally written documents such as news articles or scientific papers . existing EE methods and datasets cannot be used in informal and noisy texts . |
| Approach: | They propose to use video transcripts as a dataset for event extraction . they demonstrate that existing state-of-the-art EE methods cannot achieve adequate performance . |
| Outcome: | The proposed dataset evaluates state-of-the-art EE methods on streamed videos on Behance . it shows that such systems cannot achieve adequate performance on the proposed dataset . |
LC4EE: LLMs as Good Corrector for Event Extraction (2024.findings-acl)
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| Challenge: | Event extraction (EE) is a critical task in natural language processing, yet deploying a practical EE system remains challenging. |
| Approach: | They propose to leverage the superior extraction capability of LLMs and instruction-following ability of LRMs to construct a robust and highly available EE system. |
| Outcome: | The proposed method can identify and correct errors in SLMs predictions based on automatically generated feedback information and improve performance. |
Boosting Event Extraction with Denoised Structure-to-Text Augmentation (2023.findings-acl)
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Bo Wang, Heyan Huang, Xiaochi Wei, Ge Shi, Xiao Liu, Chong Feng, Tong Zhou, Shuaiqiang Wang, Dawei Yin
| 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. |
TextEE: Benchmark, Reevaluation, Reflections, and Future Challenges in Event Extraction (2024.findings-acl)
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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
| 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. |
AniEE: A Dataset of Animal Experimental Literature for Event Extraction (2023.findings-emnlp)
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| Challenge: | Event extraction (EE) is a crucial information extraction task in biomedical domain . existing biomedically EE datasets focus on cell experiments or overall procedures . |
| Approach: | They propose an animal experiment customized entity and event scheme for event extraction . they create an expert-annotated high-quality dataset containing discontinuous entities and nested events . |
| Outcome: | The proposed dataset is based on the animal experiment stage and a NER and EE model. |
DICE: Data-Efficient Clinical Event Extraction with Generative Models (2023.acl-long)
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| Challenge: | EE tasks target specific domains with vague entity boundaries, resulting in a lack of training data. |
| Approach: | They propose a robust and data-efficient generative model for clinical event extraction . they frame event extraction as a conditional generation problem and introduce a contrastive learning objective to decide the boundaries of biomedical mentions. |
| Outcome: | The proposed model is robust and data-efficient for clinical event extraction . it trains an auxiliary mention identification task and event extraction tasks to better identify entity mention boundaries . |
Schema-based Data Augmentation for Event Extraction (2024.lrec-main)
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| Challenge: | Existing data augmentation methods rely on language models to train event extraction models. |
| Approach: | They propose a schema-based data augmentation method that utilizes event schemas to guide the data generation process. |
| Outcome: | The proposed method produces high-quality generated data and significantly improves model performance. |