Adaptive Stochastic Dual Coordinate Ascent for Conditional Random Fields

12/22/2017

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by   Rémi Le Priol, et al.

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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.