# Bregman Conditional Random Fields: Sequence Labeling with Parallelizable Inference Algorithms

arXiv: https://arxiv.org/abs/2506.00732 · HTML: https://mlsanity.com/paper/2506.00732/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 66 references that are in the map.
- First posted on arXiv 31 May 2025.

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distances](https://mlsanity.com/paper/1306.0895/) (2013)
- [Parallelizing Linear Transformers with the Delta Rule over Sequence Length](https://mlsanity.com/paper/2406.06484/) (2024)
- [Universal Dependencies v2: An Evergrowing Multilingual Treebank Collection](https://mlsanity.com/paper/2004.10643/) (2020)
- [Learning with Fenchel-Young Losses](https://mlsanity.com/paper/1901.02324/) (2019)
- [SparseMAP: Differentiable Sparse Structured Inference](https://mlsanity.com/paper/1802.04223/) (2018)
- [Differentiable Dynamic Programming for Structured Prediction and Attention](https://mlsanity.com/paper/1802.03676/) (2018)
- [A Regularized Framework for Sparse and Structured Neural Attention](https://mlsanity.com/paper/1705.07704/) (2017)
- [Constituent Parsing as Sequence Labeling](https://mlsanity.com/paper/1810.08994/) (2018)
- [Differentiable Clustering with Perturbed Spanning Forests](https://mlsanity.com/paper/2305.16358/) (2023)
- [A Simple and Accurate Syntax-Agnostic Neural Model for Dependency-based Semantic Role Labeling](https://mlsanity.com/paper/1701.02593/) (2017)
- [Learning A Unified Named Entity Tagger From Multiple Partially Annotated Corpora For Efficient Adaptation](https://mlsanity.com/paper/1909.11535/) (2019)
- [Composing Finite State Transducers on GPUs](https://mlsanity.com/paper/1805.06383/) (2018)
- [A fast and sound tagging method for discontinuous named-entity recognition](https://mlsanity.com/paper/2409.16243/) (2024)
- [AIN: Fast and Accurate Sequence Labeling with Approximate Inference Network](https://mlsanity.com/paper/2009.08229/) (2020)
- [GPU-Accelerated Forward-Backward algorithm with Application to Lattice-Free MMI](https://mlsanity.com/paper/2112.00709/) (2021)

## Built on by (most cited first, 1 of 1)

- [Diffusion-MF: Approximate Structured Diffusion for Sequence Labelling](https://mlsanity.com/paper/2606.18856/) (2026)

## Abstract (arXiv)

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.