Fast Newton-CG Method for Batch Learning of Conditional Random Fields

We propose a fast batch learning method for linearchain Conditional Random Fields (CRFs) based on Newton-CG methods. Newton-CG methods are a variant of Newton method for high-dimensional problems. They only require the Hessian-vector products instead of the full Hessian matrices. To speed up Newton-CG methods for the CRF learning, we derive a novel dynamic programming procedure for the Hessian-vector products of the CRF objective function. The proposed procedure can reuse the byproducts of the time-consuming gradient computation for the Hessian-vector products to drastically reduce the total computation time of the Newton-CG methods. In experiments with tasks in natural language processing, the proposed method outperforms a conventional quasi-Newton method. Remarkably, the proposed method is competitive with online learning algorithms that are fast but unstable.

Authors

• Yuta Tsuboi IBM Research - Tokyo
• Yuya Unno Preferred Infrastructure, Inc.
• Hisashi Kashima Kyoto University
• Naoaki Okazaki Tokyo Institute of Technology

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