Tag infilling for Vietnamese administrative forms: Leveraging synthetic datasets and domain-specific modelling

Automatic identification of administrative tags is vital for automating administrative form processing. Despite its importance, this task remains underexplored in low-resource languages and domain-specific settings, especially in the context of Vietnamese government documents.In this article, we introduce a comprehensive and scalable pipeline for assisting constrained administrative tag infilling in Vietnamese administrative forms, comprising synthetic dataset construction, domain-adapted model training, and robust evaluation strategies.This forms a complete pipeline that predicts the appropriate administrative tag from a predefined tag set for a variety of forms generated from authentic Vietnamese administrative documents.Using a large-scale dataset of 50,000 synthetic Vietnamese administrative forms annotated with 425 distinct tags and structured into five levels of complexity, our experiments show that domain-adapted Transformer-based models consistently outperform general-purpose zero-shot large language models on seen data and generalise well to unseen inputs.This highlights the potential of our approach to achieve high accuracy with substantially smaller models, offering a more practical and efficient solution for processing Vietnamese administrative forms.