Beyond Single-Event Extraction: Towards Efficient Document-Level Multi-Event Argument Extraction

• URL: http://arxiv.org/abs/2405.01884v2
• Date: Sun, 16 Jun 2024 13:26:37 GMT
• Title: Beyond Single-Event Extraction: Towards Efficient Document-Level Multi-Event Argument Extraction
• Authors: Wanlong Liu, Li Zhou, Dingyi Zeng, Yichen Xiao, Shaohuan Cheng, Chen Zhang, Grandee Lee, Malu Zhang, Wenyu Chen,
• Abstract summary: We propose a multiple-event argument extraction model DEEIA. It is capable of extracting arguments from all events within a document simultaneously. Our method achieves new state-of-the-art performance on four public datasets.
• Score: 19.51890490853855
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent mainstream event argument extraction methods process each event in isolation, resulting in inefficient inference and ignoring the correlations among multiple events. To address these limitations, here we propose a multiple-event argument extraction model DEEIA (Dependency-guided Encoding and Event-specific Information Aggregation), capable of extracting arguments from all events within a document simultaneouslyThe proposed DEEIA model employs a multi-event prompt mechanism, comprising DE and EIA modules. The DE module is designed to improve the correlation between prompts and their corresponding event contexts, whereas the EIA module provides event-specific information to improve contextual understanding. Extensive experiments show that our method achieves new state-of-the-art performance on four public datasets (RAMS, WikiEvents, MLEE, and ACE05), while significantly saving the inference time compared to the baselines. Further analyses demonstrate the effectiveness of the proposed modules.

Related papers

• MAVEN-Arg: Completing the Puzzle of All-in-One Event Understanding Dataset with Event Argument Annotation [104.6065882758648]
  MAVEN-Arg is the first all-in-one dataset supporting event detection, event argument extraction, and event relation extraction. As an EAE benchmark, MAVEN-Arg offers three main advantages: (1) a comprehensive schema covering 162 event types and 612 argument roles, all with expert-written definitions and examples; (2) a large data scale, containing 98,591 events and 290,613 arguments obtained with laborious human annotation; and (3) the exhaustive annotation supporting all task variants of EAE.
  arXiv  Detail & Related papers  (2023-11-15T16:52:14Z)
• Type-aware Decoding via Explicitly Aggregating Event Information for Document-level Event Extraction [11.432496741340334]
  Document-level event extraction faces two main challenges: arguments-scattering and multi-event. This paper proposes a novel-based Explicitly Aggregating(SEA) model to address these limitations. SEA aggregates event information into event type and role representations, enabling the decoding of event records based on specific type-aware representations.
  arXiv  Detail & Related papers  (2023-10-16T15:10:42Z)
• Token-Event-Role Structure-based Multi-Channel Document-Level Event Extraction [15.02043375212839]
  This paper introduces a novel framework for document-level event extraction, incorporating a new data structure called token-event-role. The proposed data structure enables our model to uncover the primary role of tokens in multiple events, facilitating a more comprehensive understanding of event relationships. The results demonstrate that our approach outperforms the state-of-the-art method by 9.5 percentage points in terms of the F1 score.
  arXiv  Detail & Related papers  (2023-06-30T15:22:57Z)
• MsPrompt: Multi-step Prompt Learning for Debiasing Few-shot Event Detection [16.98619925632727]
  Event detection (ED) aims to identify the key trigger words in unstructured text and predict the event types accordingly. Traditional ED models are too data-hungry to accommodate real applications with scarce labeled data. We propose a multi-step prompt learning model (MsPrompt) for debiasing few-shot event detection.
  arXiv  Detail & Related papers  (2023-05-16T10:19:12Z)
• Abnormal Event Detection via Hypergraph Contrastive Learning [54.80429341415227]
  Abnormal event detection plays an important role in many real applications. In this paper, we study the unsupervised abnormal event detection problem in Attributed Heterogeneous Information Network. A novel hypergraph contrastive learning method, named AEHCL, is proposed to fully capture abnormal event patterns.
  arXiv  Detail & Related papers  (2023-04-02T08:23:20Z)
• EA$^2$E: Improving Consistency with Event Awareness for Document-Level Argument Extraction [52.43978926985928]
  We introduce the Event-Aware Argument Extraction (EA$2$E) model with augmented context for training and inference. Experiment results on WIKIEVENTS and ACE2005 datasets demonstrate the effectiveness of EA$2$E.
  arXiv  Detail & Related papers  (2022-05-30T04:33:51Z)
• PILED: An Identify-and-Localize Framework for Few-Shot Event Detection [79.66042333016478]
  In our study, we employ cloze prompts to elicit event-related knowledge from pretrained language models. We minimize the number of type-specific parameters, enabling our model to quickly adapt to event detection tasks for new types.
  arXiv  Detail & Related papers  (2022-02-15T18:01:39Z)
• Learning Constraints and Descriptive Segmentation for Subevent Detection [74.48201657623218]
  We propose an approach to learning and enforcing constraints that capture dependencies between subevent detection and EventSeg prediction. We adopt Rectifier Networks for constraint learning and then convert the learned constraints to a regularization term in the loss function of the neural model.
  arXiv  Detail & Related papers  (2021-09-13T20:50:37Z)
• Document-level Event Extraction with Efficient End-to-end Learning of Cross-event Dependencies [37.96254956540803]
  We propose an end-to-end model leveraging Deep Value Networks (DVN), a structured prediction algorithm, to efficiently capture cross-event dependencies for document-level event extraction. Our approach achieves comparable performance to CRF-based models on ACE05, while enjoys significantly higher computational efficiency.
  arXiv  Detail & Related papers  (2020-10-24T05:28:16Z)
• Detecting Ongoing Events Using Contextual Word and Sentence Embeddings [110.83289076967895]
  This paper introduces the Ongoing Event Detection (OED) task. The goal is to detect ongoing event mentions only, as opposed to historical, future, hypothetical, or other forms or events that are neither fresh nor current. Any application that needs to extract structured information about ongoing events from unstructured texts can take advantage of an OED system.
  arXiv  Detail & Related papers  (2020-07-02T20:44:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.