"Towards Resource-Constrained Event Extraction: A Knowledge-Augmented Framework for Overcoming Challenges in Vietnamese NLP," by Dung-Cam Quang, Dr. Xuan-Bach Le and Assoc. Prof. Tho Quan, appears in the International Journal of Innovative Computing (IJIC), volume 16, issue 1, pages 79–85, June 2026. Event extraction is a core NLP task, capturing the meaningful activities described in a document. Recent work has applied large language models to it, but the computational overhead remains a real obstacle. Worse, most state-of-the-art methods target high-resource languages such as English and Chinese, leaving low-resource languages like Vietnamese largely unexplored. The authors propose a framework built on small language models and augmented with external knowledge, aimed at three core difficulties: rare events, semantic ambiguity, and long-range dependencies. The result is a framework that is both efficient and robust enough for the Vietnamese low-resource setting. Paper: https://doi.org/10.11113/ijic.v16n1.676