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{\%} F 0.5 score on the benchmark CoNLL-2014 test set, an improvement of 3.62{\%} F 0.5 over the best previous published score.
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CoNLL-2014 Shared Task: Grammatical Error Correction
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