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Computer Science & Information Technology (CS & IT)
Most supervised systems of event detection (ED) task reply heavily on manual annotations and suffer from high-cost human effort when applied to new event types. To tackle this general problem, we turn our attention to few-shot learning (FSL). As a typical solution to FSL, cross-modal feature generation based frameworks achieve promising performance on images classification, which inspires us to advance this approach to ED task. In this work, we propose a model which extracts latent semanticdoi:10.5121/csit.2020.101402 fatcat:lnyfcs2p6nhdhd5csxmxjvu3vq