Are Triggers Needed for Document-Level Event Extraction?

TL;DR

This paper investigates whether trigger detection is necessary for document-level event extraction, revealing that its usefulness varies with dataset characteristics and that triggers influence prompt-based learning even when randomly assigned.

Contribution

It is the first study to analyze the role of triggers in document-level event extraction across multiple models and datasets, challenging assumptions from sentence-level extraction.

Findings

Trigger usefulness depends on dataset and task-specific info.

Random triggers still impact prompt-based learning.

Explicit trigger extraction is not always necessary.

Abstract

Most existing work on event extraction has focused on sentence-level texts and presumes the identification of a trigger-span -- a word or phrase in the input that evokes the occurrence of an event of interest. Event arguments are then extracted with respect to the trigger. Indeed, triggers are treated as integral to, and trigger detection as an essential component of, event extraction. In this paper, we provide the first investigation of the role of triggers for the more difficult and much less studied task of document-level event extraction. We analyze their usefulness in multiple end-to-end and pipelined transformer-based event extraction models for three document-level event extraction datasets, measuring performance using triggers of varying quality (human-annotated, LLM-generated, keyword-based, and random). We find that whether or not systems benefit from explicitly extracting triggers depends both on dataset characteristics (i.e. the typical number of events per document) and task-specific information available during extraction (i.e. natural language event schemas). Perhaps surprisingly, we also observe that the mere existence of triggers in the input, even random ones, is important for prompt-based in-context learning approaches to the task.