Trigger-free Event Detection via Derangement Reading Comprehension

TL;DR

This paper introduces a trigger-free event detection method using a derangement mechanism within a reading comprehension framework, effectively addressing challenges in low-resource scenarios without relying on costly trigger annotations.

Contribution

It proposes a novel trigger-free event detection approach with a derangement module, enabling balanced learning and improved performance in low-resource, trigger-free settings.

Findings

Achieves competitive performance with trigger-based models

Effectively handles multi-label classification and imbalanced data

Demonstrates strong results on low-resource event detection

Abstract

Event detection (ED), aiming to detect events from texts and categorize them, is vital to understanding actual happenings in real life. However, mainstream event detection models require high-quality expert human annotations of triggers, which are often costly and thus deter the application of ED to new domains. Therefore, in this paper, we focus on low-resource ED without triggers and aim to tackle the following formidable challenges: multi-label classification, insufficient clues, and imbalanced events distribution. We propose a novel trigger-free ED method via Derangement mechanism on a machine Reading Comprehension (DRC) framework. More specifically, we treat the input text as Context and concatenate it with all event type tokens that are deemed as Answers with an omitted default question. So we can leverage the self-attention in pre-trained language models to absorb semantic relations between input text and the event types. Moreover, we design a simple yet effective event derangement module (EDM) to prevent major events from being excessively learned so as to yield a more balanced training process. The experiment results show that our proposed trigger-free ED model is remarkably competitive to mainstream trigger-based models, showing its strong performance on low-source event detection.