Zero-Shot Event Detection Based on Ordered Contrastive Learning and Prompt-Based Prediction

Anonymous

Paper Link: https://openreview.net/forum?id=0GG_keLW2p5

Paper Type: Long paper (up to eight pages of content + unlimited references and appendices)

Abstract: Event detection is a classic natural language processing task. However, the constantly emerging new events make supervised methods not applicable to unseen types. Previous zero-shot event detection methods either require predefined event types as heuristic rules or resort to external semantic analyzing tools. To overcome this weakness, we propose an end-to-end framework named Zero-Shot Event Detection Based on Ordered Contrastive Learning and Prompt-Based Prediction (ZEOP). By creatively introducing multiple contrastive samples with ordered similarities, the encoder can learn event representations from both instance-level and class-level, which makes the distinctions between different unseen types more significant. Meanwhile, we utilize the prompt-based prediction to identify trigger words without relying on external resources. Experiments demonstrate that our model detects events more effectively and accurately than state-of-the-art methods.

Presentation Mode: This paper will be presented virtually

Virtual Presentation Timezone: UTC+8

Copyright Consent Signature (type Name Or NA If Not Transferrable): Senhui Zhang

Copyright Consent Name And Address: East China Normal University, Shanghai 200062, China