  This work investigates the training of conditional random fields (CRFs) via
the stochastic dual coordinate ascent (SDCA) algorithm of Shalev-Shwartz and
Zhang (2016). SDCA enjoys a linear convergence rate and a strong empirical
performance for binary classification problems. However, it has never been used
to train CRFs. 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 CRFs, and we enhance it with an adaptive non-uniform
sampling strategy based on block duality gaps. We perform experiments on four
standard sequence prediction tasks. SDCA demonstrates performances on par with
the state of the art, and improves over it on three of the four datasets, which
have in common the use of sparse features.
