SNaRe: Domain-aware Data Generation for Low-Resource Event Detection

Tanmay Parekh, Yuxuan Dong, Lucas Bandarkar, Artin Kim, I.-Hung Hsu, Kai-Wei Chang, and Nanyun Peng, in EMNLP, 2025.

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Abstract

Event detection (ED) is important for reasoning in specialized domains such as biomedicine, law and epidemiology, but existing generation approaches suffer from label noise and domain drift when applied to specialized domains. This paper introduces SNaRe, a domain-aware synthetic data generation framework with three components: Scout, Narrator and Refiner. Scout extracts triggers from unlabeled target domain data and curates a high-quality domain-specific trigger list. Narrator uses these triggers to generate domain-aligned sentences, and Refiner identifies additional event mentions to ensure annotation quality. Experiments on diverse ED datasets show that SNaRe outperforms baselines with 3-7% F1 gains in zero-/few-shot settings and 4-20% improvements in multilingual generation.

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Bib Entry

@inproceedings{parekh2025snare,
  title = {SNaRe: Domain-aware Data Generation for Low-Resource Event Detection},
  author = {Parekh, Tanmay and Dong, Yuxuan and Bandarkar, Lucas and Kim, Artin and Hsu, I-Hung and Chang, Kai-Wei and Peng, Nanyun},
  booktitle = {EMNLP},
  year = {2025}
}