FIG: Forward-Inverse Generation for Low-Resource Domain-specific Event Detection
Journal:
arXiv
Published Date:
Feb 24, 2025
Abstract
Event Detection (ED) is the task of identifying typed event mentions of
interest from natural language text, which benefits domain-specific reasoning
in biomedical, legal, and epidemiological domains. However, procuring
supervised data for thousands of events for various domains is a laborious and
expensive task. To this end, existing works have explored synthetic data
generation via forward (generating labels for unlabeled sentences) and inverse
(generating sentences from generated labels) generations. However, forward
generation often produces noisy labels, while inverse generation struggles with
domain drift and incomplete event annotations. To address these challenges, we
introduce FIG, a hybrid approach that leverages inverse generation for
high-quality data synthesis while anchoring it to domain-specific cues
extracted via forward generation on unlabeled target data. FIG further enhances
its synthetic data by adding missing annotations through forward
generation-based refinement. Experimentation on three ED datasets from diverse
domains reveals that FIG outperforms the best baseline achieving average gains
of 3.3% F1 and 5.4% F1 in the zero-shot and few-shot settings respectively.
Analyzing the generated trigger hit rate and human evaluation substantiates
FIG's superior domain alignment and data quality compared to existing
baselines.
