Minibatch and Parallelization for Online Large-Margin Structured Learning

Kai Zhao and Liang Huang

Online learning algorithms such as perceptron and MIRA have become popular for many NLP tasks thanks to their simpler architect-ure and faster convergence over batch learning methods. However, while batch learning such as CRF is easily parallelizable, online learning is much harder to parallelize: previous efforts often witness a decrease in the converged accuracy, and the speedup is typically very small (∼3) even with many (10+) processors. We instead present a much simpler architecture based on “mini-batches”, which is trivially parallelizable. We show that, unlike previous methods, minibatch learning (in serial mode) actually improves the converged accuracy for both perceptron and MIRA learning, and when combined with simple parallelization, minibatch leads to very significant speedups (up to 9x on 12 processors) on state-of-the-art parsing and tagging systems.

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