In this work, we study parameter tuning towards the M$^2$ metric, the standard metric for automatic grammar error correction (GEC) tasks. After implementing M$^2$ as a scorer in the Moses tuning framework, we investigate interactions of dense and sparse features, different optimizers, and tuning strategies for the CoNLL-2014 shared task. We notice erratic behavior when optimizing sparse feature weights with M$^2$ and offer partial solutions. To our surprise, we find that a bare-bones phrase-based SMT setup with task-specific parameter-tuning outperforms all previously published results for the CoNLL-2014 test set by a large margin (46.37% M$^2$ over previously 40.56%, by a neural encoder-decoder model) while being trained on the same data. Our newly introduced dense and sparse features widen that gap, and we improve the state-of-the-art to 49.49% M$^2$.

本研究通过在Moses tuning framework中应用M^2作为打分标准来研究自动语法纠错（GEC）任务的参数调整，发现对于纠错的稀疏特征进行优化时存在奇怪的行为，并提出了部分解决方案。最终在CoNLL-2014测试集上，我们引入了新的密集和稀疏特征，将现有的GEC技术水平提高至49.49% M^2，超过以往文献的41.75%的最高性能水平。

基于短语的机器翻译是自动语法错误修正的最先进技术