Event Extraction (EE) is crucial for NLP, capturing meaningful activities in documents. While recent research has applied Large Language Models to EE, their computational overhead remains problematic. Moreover, existing state-of-the-art methods predominantly focus on high-resource languages such as English and Chinese, leaving low-resource languages, like Vietnamese, largely under-explored due to unique linguistic challenges. We propose a Small Language Model-based framework enhanced with external knowledge to address Vietnamese EE. The approach targets core difficulties including rare events, semantic ambiguity, and long-range dependencies, thereby establishing an efficient and robust framework specifically tailored for the Vietnamese low-resource language domain.
@article{quang2026event,
author = {Quang, Dung-Cam and Le, Xuan-Bach and Quan, Tho},
title = {Towards Resource-Constrained Event Extraction: A Knowledge-Augmented Framework for Overcoming Challenges in Vietnamese NLP},
journal = {International Journal of Innovative Computing (IJIC)},
volume = {16},
number = {1},
pages = {79--85},
year = {2026},
doi = {10.11113/ijic.v16n1.676}
}