MsPrompt: Multi-step Prompt Learning for Debiasing Few-shot Event Detection

• URL: http://arxiv.org/abs/2305.09335v1
• Date: Tue, 16 May 2023 10:19:12 GMT
• Title: MsPrompt: Multi-step Prompt Learning for Debiasing Few-shot Event Detection
• Authors: Siyuan Wang, Jianming Zheng, Xuejun Hu, Fei Cai, Chengyu Song, Xueshan Luo
• Abstract summary: 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.
• Score: 16.98619925632727
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Event detection (ED) is aimed 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. Besides, typical ED models are facing the context-bypassing and disabled generalization issues caused by the trigger bias stemming from ED datasets. Therefore, we focus on the true few-shot paradigm to satisfy the low-resource scenarios. In particular, we propose a multi-step prompt learning model (MsPrompt) for debiasing few-shot event detection, that consists of the following three components: an under-sampling module targeting to construct a novel training set that accommodates the true few-shot setting, a multi-step prompt module equipped with a knowledge-enhanced ontology to leverage the event semantics and latent prior knowledge in the PLMs sufficiently for tackling the context-bypassing problem, and a prototypical module compensating for the weakness of classifying events with sparse data and boost the generalization performance. Experiments on two public datasets ACE-2005 and FewEvent show that MsPrompt can outperform the state-of-the-art models, especially in the strict low-resource scenarios reporting 11.43% improvement in terms of weighted F1-score against the best-performing baseline and achieving an outstanding debiasing performance.

Related papers

• Beyond Single-Event Extraction: Towards Efficient Document-Level Multi-Event Argument Extraction [19.51890490853855]
  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.
  arXiv  Detail & Related papers  (2024-05-03T07:04:35Z)
• Boosting Event Extraction with Denoised Structure-to-Text Augmentation [52.21703002404442]
  Event extraction aims to recognize pre-defined event triggers and arguments from texts. Recent data augmentation methods often neglect the problem of grammatical incorrectness. We propose a denoised structure-to-text augmentation framework for event extraction DAEE.
  arXiv  Detail & Related papers  (2023-05-16T16:52:07Z)
• 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)
• HCL-TAT: A Hybrid Contrastive Learning Method for Few-shot Event Detection with Task-Adaptive Threshold [18.165302114575212]
  We propose a novel Hybrid Contrastive Learning method with a Task-Adaptive Threshold (abbreviated as HCLTAT) In this paper, we propose a novel Hybrid Contrastive Learning method with a Task-Adaptive Threshold (abbreviated as HCLTAT), which enables discriminative representation learning with a two-view contrastive loss. Experiments on the benchmark dataset FewEvent demonstrate the superiority of our method to achieve better results compared to the state-of-the-arts.
  arXiv  Detail & Related papers  (2022-10-17T07:37:38Z)
• 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)
• 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)
• Robust Event Classification Using Imperfect Real-world PMU Data [58.26737360525643]
  We study robust event classification using imperfect real-world phasor measurement unit (PMU) data. We develop a novel machine learning framework for training robust event classifiers.
  arXiv  Detail & Related papers  (2021-10-19T17:41:43Z)
• 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)
• Adaptive Knowledge-Enhanced Bayesian Meta-Learning for Few-shot Event Detection [34.0901494858203]
  Event detection (ED) aims at detecting event trigger words in sentences and classifying them into specific event types. We propose a knowledge-based few-shot event detection method which uses a definition-based encoder to introduce external event knowledge. Experiments show our method consistently and substantially outperforms a number of baselines by at least 15 absolute F1 points.
  arXiv  Detail & Related papers  (2021-05-20T04:26:26Z)
• 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.