Nicolas Floquet | Searcharxiv

arXiv2026

Diffusion-MF: Approximate Structured Diffusion for Sequence Labelling

We introduce Diffusion-MF, a discrete diffu- sion sequence labeller that places a linear-chain conditional random field (LCRF) inside the denoising loop. Unlike prior diffusion labellers, it performs structured inference at every step; parallel Mean-Field makes this efficient. Across multilingual POS, CoNLL-2003 NER, and joint Chinese Segmentation and POS, Diffusion-MF achieves the best primary result across all experimental settings.

cs.CL↗

arXiv2025

Scaling Graph-Based Dependency Parsing with Arc Vectorization and Attention-Based Refinement

We propose a novel architecture for graph-based dependency parsing that explicitly constructs vectors, from which both arcs and labels are scored. Our method addresses key limitations of the standard two-pipeline approach by unifying arc scoring and labeling into a single network, reducing scalability issues caused by the information bottleneck and lack of parameter sharing. Additionally, our architecture overcomes limited arc interactions with transformer layers to efficiently simulate higher-order dependencies. Experiments on PTB and UD show that our model outperforms state-of-the-art parsers in both accuracy and efficiency.

cs.CL↗