Probing and Fine-tuning Reading Comprehension Models for Few-shot Event Extraction

Understand

We study the problem of event extraction from text data, which requires both detecting target event types and their arguments.

• Typically, both the event detection and argument detection subtasks are formulated as supervised sequence labeling problems.
• We argue that the event extraction models so trained are inherently label-hungry, and can generalize poorly across domains and text genres.We propose a reading comprehension framework for event extraction.Specifically, we formulate event detection as a textual entailment prediction problem, and argument detection as a question answer-ing problem.
• By constructing proper query templates, our approach can effectively distill rich knowledge about tasks and label semantics from pretrained reading comprehension models.