Information extraction (IE) systems rely on structured data for training, but such annotated data is highly imbalanced across languages, with low-resource languages receiving little attention.Label projection techniques aim to bridge this gap by transferring structured annotations from high-resource to low-resource languages.However, existing methods are either inaccurate or too slow for large-scale use.This work aims to address this problem by developing a more effective method that remains sufficiently efficient for large-scale projection.In particular, we propose to synthesize alignment sequence pairs and fine-tune an encoder model with span alignment objective, while controlling data influence during training.Experimental results across 50+ languages show that our framework consistently outperforms previous state-of-the-art methods while maintaining fast inference speed.In addition, we introduce EXP -the first benchmark for explicit evaluation of label projection, thereby reducing confounders and non-determinism in method assessment.

Chien Van Nguyen, Huy Huu Nguyen, Ruiyi Zhang, Hanieh Deilamsalehy, Puneet Mathur, Viet Dac Lai, Haoliang Wang, Jayakumar Subramanian, Ryan A. Rossi, Trung Bui, Nikos Vlassis, Franck Dernoncourt, Thien Huu Nguyen. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.