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Approximate Structured Diffusion for Sequence Labelling
[Submitted on 17 Jun 2026] · 2026-06-18 · via cs updates on arXiv.org

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Abstract:Sequence labelling, a core task of Natural Language Processing (NLP), consists in assigning each token of an input sentence a label.
From a Machine Learning point of view, sequence labelling is often cast as a Linear-Chain Conditional Random Field (CRF) parametrised by a neural network.
While this approach gives good empirical results, CRFs assume a finite decision span (eg label bigrams) which can limit their expressivity and hurt performance when long-range dependencies are required.
We show we can leverage diffusion to train a CRF conditioned on an entire label sequence, with the caveat that the condition is on a noisy version of labels.
We show experimentally that this method, in conjunction with approximate CRF inference, improves label accuracy with a 16.5% error reduction for POS-tagging.

Submission history

From: Joseph Le Roux [view email]
[v1] Wed, 17 Jun 2026 09:36:34 UTC (66 KB)