# Lock-Free Parallel Perceptron for Graph-based Dependency Parsing

## TL;DR

This paper introduces a lock-free parallel perceptron algorithm for graph-based dependency parsing, significantly accelerating training on multi-core systems without sacrificing accuracy.

## Contribution

It presents a novel parallel perceptron algorithm that reduces training time for dependency parsing by leveraging multi-core architectures.

## Key findings

- 8-fold faster training with 10 threads
- No loss in parsing accuracy
- Effective utilization of multi-core systems

## 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.