AI Insight
This paper presents Diffusion-MF, a new approach to sequence labeling tasks that combines discrete diffusion models with linear-chain conditional random fields. The key innovation is incorporating structured inference using parallel Mean-Field approximation at each step of the denoising process, rather than making independent predictions. The method demonstrates superior performance across multiple benchmark tasks including part-of-speech tagging in multiple languages, named entity recognition on CoNLL-2003, and joint Chinese word segmentation with part-of-speech tagging.
Why it matters
Improved sequence labeling has direct applications in natural language processing pipelines, including automated text analysis, information extraction, and language understanding systems. The method's consistent performance gains across diverse languages and tasks suggests it could become a more reliable foundation for multilingual NLP applications.
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⚠️ Preprint – Noch nicht peer-reviewed
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Abstract: 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.
Source: Diffusion-MF: Approximate Structured Diffusion for Sequence Labelling