Adaptive Stochastic Dual Coordinate Ascent for Conditional Random Fields

By Rémi Le Priol, Alexandre Piché, Simon Lacoste-Julien. 2017

7 citations recorded by the source corpus.

This work investigates training Conditional Random Fields (CRF) by Stochastic Dual Coordinate Ascent (SDCA). SDCA enjoys a linear convergence rate and a strong empirical performance for independent classification problems. However, it has never been used to train CRF. Yet it benefits from an exact line search with a single marginalization oracle call, unlike previous approaches. In this paper, we adapt SDCA to train CRF and we enhance it with an adaptive non-uniform sampling strategy. Our preliminary experiments suggest that this method matches state-of-the-art CRF optimization techniques.

Subfields: Natural Language Processing

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Citations in the corpus

Structured Convolutional Kernel Networks for Airline Crew Scheduling (2021, methodology): “To train CRFs, we either use a Structured SVM solver using BCFW algorithm (Lacoste-Julien et al., 2013), or a stochastic optimization algorithm using SDCA (Le Priol et al., 2018).”