Learning Event Representations for Zero-Shot Detection via Dual-Contrastive Prompting

Jiaxu Li, Bin Ge (), Hao Xu, Peixin Huang and Hongbin Huang
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Jiaxu Li: Laboratory for Big Data and Decision, National University of Defense Technology, Changsha 410073, China
Bin Ge: Laboratory for Big Data and Decision, National University of Defense Technology, Changsha 410073, China
Hao Xu: Laboratory for Big Data and Decision, National University of Defense Technology, Changsha 410073, China
Peixin Huang: Laboratory for Big Data and Decision, National University of Defense Technology, Changsha 410073, China
Hongbin Huang: Laboratory for Big Data and Decision, National University of Defense Technology, Changsha 410073, China

Mathematics, 2024, vol. 12, issue 9, 1-18

Abstract: Zero-shot event detection aims to involve the automatic discovery and classification of new events within unstructured text. Current zero-shot event detection methods have not considered addressing the problem more effectively from the perspective of improving event representations. In this paper, we propose dual-contrastive prompting (COPE) model for learning event representations to address zero-shot event detection, which leverages prompts to assist in generating event embeddings using a pretrained language model, and employs a contrastive fusion approach to capture complex interaction information between trigger representations and sentence embeddings to obtain enhanced event representations. Firstly, we introduce a sample generator to create ordered contrastive sample sequences with varying degrees of similarity for each event instance, aiding the model in better distinguishing different types of events. Secondly, we design two distinct prompts to obtain trigger representations and event sentence embeddings separately. Thirdly, we employ a contrastive fusion module, where trigger representations and event sentence embeddings interactively fuse in vector space to generate the final event representations. Experiments show that our model is more effective than the most advanced methods.

Keywords: event detection; event representations; dual-contrastive learning; contrastive fusion (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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