Phrase-based Machine Translation is State-of-the-Art for Automatic Grammatical Error Correction

Understand

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.
• 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 41.75%, by an SMT system with neural features) while being trained on the same, publicly available data.