Probing and Fine-tuning Reading Comprehension Models for Few-shot Event Extraction

• URL: http://arxiv.org/abs/2010.11325v1
• Date: Wed, 21 Oct 2020 21:48:39 GMT
• Title: Probing and Fine-tuning Reading Comprehension Models for Few-shot Event Extraction
• Authors: Rui Feng, Jie Yuan, Chao Zhang
• Abstract summary: We propose a reading comprehension framework for event extraction. By constructing proper query templates, our approach can effectively distill rich knowledge about tasks and label semantics. Our method achieves state-of-the-art performance on the ACE 2005 benchmark when trained with full supervision.
• Score: 17.548548562222766
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We study the problem of event extraction from text data, which requires both detecting target event types and their arguments. Typically, both the event detection and argument detection subtasks are formulated as supervised sequence labeling problems. We argue that the event extraction models so trained are inherently label-hungry, and can generalize poorly across domains and text genres.We propose a reading comprehension framework for event extraction.Specifically, we formulate event detection as a textual entailment prediction problem, and argument detection as a question answer-ing problem. By constructing proper query templates, our approach can effectively distill rich knowledge about tasks and label semantics from pretrained reading comprehension models. Moreover, our model can be fine-tuned with a small amount of data to boost its performance. Our experiment results show that our method performs strongly for zero-shot and few-shot event extraction, and it achieves state-of-the-art performance on the ACE 2005 benchmark when trained with full supervision.

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)
• Semantic Pivoting Model for Effective Event Detection [19.205550116466604]
  Event Detection aims to identify and classify mentions of event instances from unstructured articles. Existing techniques for event detection only use homogeneous one-hot vectors to represent the event type classes, ignoring the fact that the semantic meaning of the types is important to the task. We propose a Semantic Pivoting Model for Effective Event Detection (SPEED), which explicitly incorporates prior information during training and captures semantically meaningful correlations between input and events.
  arXiv  Detail & Related papers  (2022-11-01T19:20:34Z)
• Trigger-free Event Detection via Derangement Reading Comprehension [4.728684358207039]
  Event detection aims to detect events from texts and categorize them. We propose a novel trigger-free ED method via Derangement mechanism on a machine Reading (DRC) framework. We show that our proposed trigger-free ED model is remarkably competitive to mainstream trigger-based models.
  arXiv  Detail & Related papers  (2022-08-20T11:01:39Z)
• Unifying Event Detection and Captioning as Sequence Generation via Pre-Training [53.613265415703815]
  We propose a unified pre-training and fine-tuning framework to enhance the inter-task association between event detection and captioning. Our model outperforms the state-of-the-art methods, and can be further boosted when pre-trained on extra large-scale video-text data.
  arXiv  Detail & Related papers  (2022-07-18T14:18:13Z)
• 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)
• Query and Extract: Refining Event Extraction as Type-oriented Binary Decoding [51.57864297948228]
  We propose a novel event extraction framework that takes event types and argument roles as natural language queries. Our framework benefits from the attention mechanisms to better capture the semantic correlation between the event types or argument roles and the input text.
  arXiv  Detail & Related papers  (2021-10-14T15:49:40Z)
• 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)
• Unsupervised Label-aware Event Trigger and Argument Classification [73.86358632937372]
  We propose an unsupervised event extraction pipeline, which first identifies events with available tools (e.g., SRL) and then automatically maps them to pre-defined event types. We leverage pre-trained language models to contextually represent pre-defined types for both event triggers and arguments. We successfully map 83% of the triggers and 54% of the arguments to the correct types, almost doubling the performance of previous zero-shot approaches.
  arXiv  Detail & Related papers  (2020-12-30T17:47:24Z)
• 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)
• Event Extraction by Answering (Almost) Natural Questions [40.13163091122463]
  We introduce a new paradigm for event extraction by formulating it as a question answering (QA) task. Empirical results demonstrate that our framework outperforms prior methods substantially.
  arXiv  Detail & Related papers  (2020-04-28T16:15:46Z)

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.