Synthetic dataset generation for theft event extraction in Italian

Event extraction is the task of automatically identifying and extracting structured information about events from unstructured text. Despite Italian being a well-resourced language, it still lacks annotated datasets specifically designed for fine-grained event extraction. To address this gap, we propose a novel methodology for the generation of synthetic data suitable for fine-grained event extraction tasks. This work is motivated by the high cost and limited scalability of manual annotation. We introduce a controlled synthetic data generation pipeline that strictly adheres to a target annotation schema, providing a scalable alternative to extensive human labeling. The key methodological innovation is a two-phase, document-level generation framework that leverages Large Language Models, ensures structural consistency and mitigates generation biases, enabling the creation of high-quality datasets for complex event extraction scenarios. Using this methodology, we release SYNTH-ITA, the first collection of four medium-scale synthetic datasets for fine-grained Italian event extraction, generated from 10,000 structured crime scenarios each. Experiments conducted on event argument extraction using a QA formulation demonstrate that fine-tuning models on SYNTH-ITA leads to better or comparable performances to models fine-tuned on 200 manually annotated real news articles (+14% improvement with ELECTRA, -0.4% with BERT). Conversely, NER-based models for event argument extraction trained on synthetic data exhibit an 18% performance drop compared to those trained on manually annotated articles.