Bregman Conditional Random Fields: Sequence Labeling with Parallelizable Inference Algorithms
Year: 2025
Type: conference-paper
Abstract: We propose a novel discriminative model for sequence labeling called Bregman conditional random fields (BCRF).Contrary to standard linear-chain conditional random fields, BCRF allows fast parallelizable inference algorithms based on iterative Bregman projections.We show how such models can be learned using Fenchel-Young losses, including extension for learning from partial labels.Experimentally, our approach delivers comparable results to CRF while being faster, and achieves better results in highly constrained settings compared to mean field, another parallelizable alternative.
Authors: Caio Corro, Mathieu Lacroix, Joseph Le Roux
Institutions: Centre National de la Recherche Scientifique, Institut national de recherche en sciences et technologies du numérique, Institut de Recherche en Informatique et Systèmes Aléatoires, Institut National des Sciences Appliquées de Rennes, LINKMEDIA: Creating and exploiting explicit links between multimedia fragments, Université de Rennes, Université Sorbonne Paris Nord, Laboratoire d'Informatique de Paris-Nord
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4412889693