Lock-Free Parallel Perceptron for Graph-based Dependency Parsing

Xu Sun; Shuming Ma

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arXiv · 1703.00782

Lock-Free Parallel Perceptron for Graph-based Dependency Parsing

Xu Sun·Shuming Ma

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Abstract

Dependency parsing is an important NLP task. A popular approach for dependency parsing is structured perceptron. Still, graph-based dependency parsing has the time complexity of $O(n^3)$, and it suffers from slow training. To deal with this problem, we propose a parallel algorithm called parallel perceptron. The parallel algorithm can make full use of a multi-core computer which saves a lot of training time. Based on experiments we observe that dependency parsing with parallel perceptron can achieve 8-fold faster training speed than traditional structured perceptron methods when using 10 threads, and with no loss at all in accuracy.

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Xu Sun, Shuming Ma. 2017-03-02. Lock-Free Parallel Perceptron for Graph-based Dependency Parsing. https://arxiv.org/abs/1703.00782

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