Document-Level Event Role Filler Extraction using Multi-Granularity Contextualized Encoding

• URL: http://arxiv.org/abs/2005.06579v1
• Date: Wed, 13 May 2020 20:42:17 GMT
• Title: Document-Level Event Role Filler Extraction using Multi-Granularity Contextualized Encoding
• Authors: Xinya Du and Claire Cardie
• Abstract summary: 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.
• Score: 40.13163091122463
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Few works in the literature of event extraction have gone beyond individual sentences to make extraction decisions. This is problematic when the information needed to recognize an event argument is spread across multiple sentences. We argue that document-level 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 (with pre-trained language model representations) perform on document-level role filler extraction, as well as how the length of context captured affects the models' performance. To dynamically aggregate information captured by neural representations learned at different levels of granularity (e.g., the sentence- and paragraph-level), we propose a novel multi-granularity reader. We evaluate our models on the MUC-4 event extraction dataset, and show that our best system performs substantially better than prior work. We also report findings on the relationship between context length and neural model performance on the task.

Related papers

• 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)
• Zero-Shot On-the-Fly Event Schema Induction [61.91468909200566]
  We present a new approach in which large language models are utilized to generate source documents that allow predicting, given a high-level event definition, the specific events, arguments, and relations between them. Using our model, complete schemas on any topic can be generated on-the-fly without any manual data collection, i.e., in a zero-shot manner.
  arXiv  Detail & Related papers  (2022-10-12T14:37:00Z)
• CLIP-Event: Connecting Text and Images with Event Structures [123.31452120399827]
  We propose a contrastive learning framework to enforce vision-language pretraining models. We take advantage of text information extraction technologies to obtain event structural knowledge. Experiments show that our zero-shot CLIP-Event outperforms the state-of-the-art supervised model in argument extraction.
  arXiv  Detail & Related papers  (2022-01-13T17:03:57Z)
• Event Argument Extraction using Causal Knowledge Structures [9.56216681584111]
  Event Argument extraction refers to the task of extracting structured information from unstructured text for a particular event of interest. Most of the existing works model this task at a sentence level, restricting the context to a local scope. We propose an external knowledge aided approach to infuse document-level event information to aid the extraction of complex event arguments.
  arXiv  Detail & Related papers  (2021-05-02T13:59:07Z)
• Narrative Incoherence Detection [76.43894977558811]
  We propose the task of narrative incoherence detection as a new arena for inter-sentential semantic understanding. Given a multi-sentence narrative, decide whether there exist any semantic discrepancies in the narrative flow.
  arXiv  Detail & Related papers  (2020-12-21T07:18:08Z)
• The Devil is in the Details: Evaluating Limitations of Transformer-based Methods for Granular Tasks [19.099852869845495]
  Contextual embeddings derived from transformer-based neural language models have shown state-of-the-art performance for various tasks. We focus on the problem of textual similarity from two perspectives: matching documents on a granular level, and an abstract level. We empirically demonstrate, across two datasets from different domains, that despite high performance in abstract document matching as expected, contextual embeddings are consistently (and at times, vastly) outperformed by simple baselines like TF-IDF for more granular tasks.
  arXiv  Detail & Related papers  (2020-11-02T18:41:32Z)
• 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)
• Topic Adaptation and Prototype Encoding for Few-Shot Visual Storytelling [81.33107307509718]
  We propose a topic adaptive storyteller to model the ability of inter-topic generalization. We also propose a prototype encoding structure to model the ability of intra-topic derivation. Experimental results show that topic adaptation and prototype encoding structure mutually bring benefit to the few-shot model.
  arXiv  Detail & Related papers  (2020-08-11T03:55:11Z)
• Pre-training for Abstractive Document Summarization by Reinstating Source Text [105.77348528847337]
  This paper presents three pre-training objectives which allow us to pre-train a Seq2Seq based abstractive summarization model on unlabeled text. Experiments on two benchmark summarization datasets show that all three objectives can improve performance upon baselines.
  arXiv  Detail & Related papers  (2020-04-04T05:06:26Z)

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.