Approximate Structured Diffusion for Sequence Labelling
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Computer Science > Computation and Language
Title:Approximate Structured Diffusion for Sequence Labelling
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.
| Subjects: | Computation and Language (cs.CL); Machine Learning (cs.LG) |
| Cite as: | arXiv:2606.18856 [cs.CL] |
| (or arXiv:2606.18856v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.18856
arXiv-issued DOI via DataCite (pending registration)
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