In this article, we present the BTransformer18 model, a deep learning architecture designed for multi-label relation extraction in French texts. Our approach combines the contextual representation capabilities of pre-trained language models from the BERT family - such as BERT, RoBERTa, and their French counterparts CamemBERT and FlauBERT - with the power of Transformer encoders to capture long-term dependencies between tokens. Experiments conducted on the dataset from the TextMine'25 challenge show that our model achieves superior performance, particularly when using CamemBERT-Large, with a macro F1 score of 0.654, surpassing the results obtained with FlauBERT-Large. These results demonstrate the effectiveness of our approach for the automatic extraction of complex relations in intelligence reports.
@article{arxiv.2502.15619,
title = {Extraction multi-\'etiquettes de relations en utilisant des couches de Transformer},
author = {Ngoc Luyen Le and Gildas Tagny Ngompé},
journal= {arXiv preprint arXiv:2502.15619},
year = {2025}
}