Reaching Human-level Performance in Automatic Grammatical Error Correction: An Empirical Study
Table 2: Comparison of GEC systems on CoNLL and JFLEG benchmark datasets.
['[BOLD] System', '[BOLD] CoNLL-2014', '[BOLD] CoNLL-10', '[BOLD] JFLEG test']
Our base convolutional seq2seq model outperforms most of previous GEC systems owing to the larger size of training data we use. Fluency boost learning further improves the base convolutional seq2seq model. It achieves 61.34 in CoNLL-2014, 76.88 F0.5 score in CoNLL-10 benchmarks, and 61.41 GLEU score on JFLEG test set. When we further add fluency boost inference, the system’s performance on JFLEG test set is improved to 62.42 GLEU score, while its F0.5 scores on CoNLL benchmarks drop.