VLSP 2025 challenge: Vietnamese Semantic Parsing - Linh, Ha My; Duc, Pham Thi; Toan, Le Ngoc; Huyen, Nguyen Thi Minh (VNU Journal of Science: Computer Science and Communication Engineering; Vol 41 No 2 (2025)DO - 10.25073/2588-1086/vnucsce.6493, 2025)
In 2025, the Eleventh Workshop on Vietnamese Language and Speech Processing (VLSP 2025) introduced its first shared task on Vietnamese Semantic Parsing, known as viSemParse. This task aims to assess how effectively participating systems can represent the deep semantic structure of Vietnamese sentences. To support model development and evaluation, the organizers created highquality, task-specific annotated datasets. The viSemParse 2025 corpus comprises 2,500 Vietnamese sentences, carefully partitioned into training, public test, and private test splits to support fair and reproducible evaluation. The shared task was conducted on the AIHub platform, where teams were required to submit predictions on the public test set before receiving their final ranking based on the hidden private test set, ensuring robustness against overfitting. The best-performing system in the viSemParse track achieved a Smatch score of 58%, a result that highlights not only the inherent complexity of semantic parsing in Vietnamese but also the substantial opportunities for methodological advances and future research in this area. Keywords: Vietnamese semantic parsing, viSemParse, VLSP 2025