A Two-Stream AMR-enhanced Model for Document-level Event Argument Extraction

• URL: http://arxiv.org/abs/2205.00241v1
• Date: Sat, 30 Apr 2022 11:17:26 GMT
• Title: A Two-Stream AMR-enhanced Model for Document-level Event Argument Extraction
• Authors: Runxin Xu, Peiyi Wang, Tianyu Liu, Shuang Zeng, Baobao Chang, Zhifang Sui
• Abstract summary: We propose a Two-Stream Abstract meaning Representation enhanced extraction model (TSAR) TSAR encodes the document from different perspectives by a two-stream encoding module. An AMR-guided interaction module captures both intra-sentential and inter-sentential features.
• Score: 32.54105023345553
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Most previous studies aim at extracting events from a single sentence, while document-level event extraction still remains under-explored. In this paper, we focus on extracting event arguments from an entire document, which mainly faces two critical problems: a) the long-distance dependency between trigger and arguments over sentences; b) the distracting context towards an event in the document. To address these issues, we propose a Two-Stream Abstract meaning Representation enhanced extraction model (TSAR). TSAR encodes the document from different perspectives by a two-stream encoding module, to utilize local and global information and lower the impact of distracting context. Besides, TSAR introduces an AMR-guided interaction module to capture both intra-sentential and inter-sentential features, based on the locally and globally constructed AMR semantic graphs. An auxiliary boundary loss is introduced to enhance the boundary information for text spans explicitly. Extensive experiments illustrate that TSAR outperforms previous state-of-the-art by a large margin, with 2.54 F1 and 5.13 F1 performance gain on the public RAMS and WikiEvents datasets respectively, showing the superiority in the cross-sentence arguments extraction. We release our code in https://github.com/ PKUnlp-icler/TSAR.

Related papers

• Spatial Semantic Recurrent Mining for Referring Image Segmentation [63.34997546393106]
  We propose Stextsuperscript2RM to achieve high-quality cross-modality fusion. It follows a working strategy of trilogy: distributing language feature, spatial semantic recurrent coparsing, and parsed-semantic balancing. Our proposed method performs favorably against other state-of-the-art algorithms.
  arXiv  Detail & Related papers  (2024-05-15T00:17:48Z)
• ULTRA: Unleash LLMs' Potential for Event Argument Extraction through Hierarchical Modeling and Pair-wise Refinement [6.39480325103865]
  Event argument extraction (EAE) is the task of identifying role-specific text spans (i.e., arguments) for a given event. We propose a hierarchical framework that extracts event arguments more cost-effectively.
  arXiv  Detail & Related papers  (2024-01-24T04:13:28Z)
• 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)
• Enhancing Document-level Event Argument Extraction with Contextual Clues and Role Relevance [12.239459451494872]
  Document-level event argument extraction poses new challenges of long input and cross-sentence inference. We propose a Span-trigger-based Contextual Pooling and latent Role Guidance model.
  arXiv  Detail & Related papers  (2023-10-08T11:29:10Z)
• An AMR-based Link Prediction Approach for Document-level Event Argument Extraction [51.77733454436013]
  Recent works have introduced Abstract Meaning Representation (AMR) for Document-level Event Argument Extraction (Doc-level EAE) This work reformulates EAE as a link prediction problem on AMR graphs. We propose a novel graph structure, Tailored AMR Graph (TAG), which compresses less informative subgraphs and edge types, integrates span information, and highlights surrounding events in the same document.
  arXiv  Detail & Related papers  (2023-05-30T16:07:48Z)
• Enriching Relation Extraction with OpenIE [70.52564277675056]
  Relation extraction (RE) is a sub-discipline of information extraction (IE) In this work, we explore how recent approaches for open information extraction (OpenIE) may help to improve the task of RE. Our experiments over two annotated corpora, KnowledgeNet and FewRel, demonstrate the improved accuracy of our enriched models.
  arXiv  Detail & Related papers  (2022-12-19T11:26:23Z)
• Dynamic Global Memory for Document-level Argument Extraction [63.314514124716936]
  We introduce a new global neural generation-based framework for document-level event argument extraction. We use a document memory store to record the contextual event information and leverage it to implicitly and explicitly help with decoding of arguments for later events. Empirical results show that our framework outperforms prior methods substantially.
  arXiv  Detail & Related papers  (2022-09-18T23:45:25Z)
• RAAT: Relation-Augmented Attention Transformer for Relation Modeling in Document-Level Event Extraction [16.87868728956481]
  We propose a new DEE framework which can model the relation dependencies, called Relation-augmented Document-level Event Extraction (ReDEE) To further leverage relation information, we introduce a separate event relation prediction task and adopt multi-task learning method to explicitly enhance event extraction performance.
  arXiv  Detail & Related papers  (2022-06-07T15:11:42Z)
• 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)
• Cross-media Structured Common Space for Multimedia Event Extraction [82.36301617438268]
  We introduce a new task, MultiMedia Event Extraction (M2E2), which aims to extract events and their arguments from multimedia documents. We propose a novel method, Weakly Aligned Structured Embedding (WASE), that encodes structured representations of semantic information into a common embedding space. By utilizing images, we extract 21.4% more event mentions than traditional text-only methods.
  arXiv  Detail & Related papers  (2020-05-05T20:21:53Z)

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.