Prompt-Guided Few-Shot Event Detection

Anonymous

Abstract: Practical applications of event extraction systems have long been hindered by their need for heavy human annotation. In order to scale up to new domains and event types, models must learn to cope with limited supervision, as in few-shot learning settings. To this end, the major challenge is to let the model master the semantic of event types, without requiring abundant event mention annotations. In our study, we employ cloze prompts to elicit event-related knowledge from pre-trained language models and further use event definitions and keywords to pinpoint the trigger word. By formulating the event detection task as an "identify-then-localize" procedure, we minimize the number of type-specific parameters, enabling our model to quickly adapt to event detection tasks for new types. Experiments on three event detection benchmark datasets (ACE, FewEvent, MAVEN) show that our proposed method performs favorably under fully supervised settings and surpasses existing few-shot methods by 16% F1 on the FewEvent dataset and 23% on the MAVEN dataset when only 5 examples are provided for each event type.