Connecting the Dots: Towards Human-Level Grammatical Error Correction

We build a grammatical error correction (GEC) system primarily based on the state-of-the-art statistical machine translation (SMT) approach, using task-specific features and tuning, and further enhance it with the modeling power of neural network joint models. The SMT-based system is weak in generalizing beyond patterns seen during training and lacks granularity below the word level. To address this issue, we incorporate a character-level SMT component targeting the misspelled words that the original SMT-based system fails to correct. Our final system achieves 53.14% F0.5 score on the benchmark CoNLL-2014 test set, an improvement of 3.62% F0.5 over the best previous published score.