Document-level Event Extraction with Efficient End-to-end Learning of Cross-event Dependencies

• URL: http://arxiv.org/abs/2010.12787v3
• Date: Fri, 7 May 2021 22:47:09 GMT
• Title: Document-level Event Extraction with Efficient End-to-end Learning of Cross-event Dependencies
• Authors: Kung-Hsiang Huang, Nanyun Peng
• Abstract summary: 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.
• Score: 37.96254956540803
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Fully understanding narratives often requires identifying events in the context of whole documents and modeling the event relations. However, document-level event extraction is a challenging task as it requires the extraction of event and entity coreference, and capturing arguments that span across different sentences. Existing works on event extraction usually confine on extracting events from single sentences, which fail to capture the relationships between the event mentions at the scale of a document, as well as the event arguments that appear in a different sentence than the event trigger. In this paper, 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. Experimental results show that our approach achieves comparable performance to CRF-based models on ACE05, while enjoys significantly higher computational efficiency.

Related papers

• Improving Event Definition Following For Zero-Shot Event Detection [66.27883872707523]
  Existing approaches on zero-shot event detection usually train models on datasets annotated with known event types. We aim to improve zero-shot event detection by training models to better follow event definitions.
  arXiv  Detail & Related papers  (2024-03-05T01:46:50Z)
• 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)
• From Simple to Complex: A Progressive Framework for Document-level Informative Argument Extraction [34.37013964529546]
  Event Argument Extraction (EAE) requires the model to extract arguments of multiple events from a single document. We propose a simple-to-complex progressive framework for document-level EAE. Our model outperforms SOTA by 1.4% in F1, indicating the proposed simple-to-complex framework is useful in the EAE task.
  arXiv  Detail & Related papers  (2023-10-25T04:38:02Z)
• 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)
• 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)
• 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)
• CasEE: A Joint Learning Framework with Cascade Decoding for Overlapping Event Extraction [9.300138832652658]
  Event extraction (EE) is a crucial information extraction task that aims to extract event information in texts. This work systematically studies the realistic event overlapping problem, where a word may serve as triggers with several types or arguments with different roles. We propose a novel joint learning framework with cascade decoding for overlapping event extraction, termed as CasEE.
  arXiv  Detail & Related papers  (2021-07-04T10:01:55Z)
• Document-Level Event Argument Extraction by Conditional Generation [75.73327502536938]
  Event extraction has long been treated as a sentence-level task in the IE community. We propose a document-level neural event argument extraction model by formulating the task as conditional generation following event templates. We also compile a new document-level event extraction benchmark dataset WikiEvents.
  arXiv  Detail & Related papers  (2021-04-13T03:36:38Z)
• Document-Level Event Role Filler Extraction using Multi-Granularity Contextualized Encoding [40.13163091122463]
  Event extraction is a difficult task since it requires a view of a larger context to determine which spans of text correspond to event role fillers. We first investigate how end-to-end neural sequence models perform on document-level role filler extraction. We show that our best system performs substantially better than prior work.
  arXiv  Detail & Related papers  (2020-05-13T20:42:17Z)

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.