Reaching Human-level Performance in Automatic Grammatical Error Correction: An Empirical Study
Table 4: A comparison of recall of the convolutional seq2seq model with/without fluency boost learning for each error type in CoNLL-2014 dataset.
['[BOLD] Error type', '[BOLD] Base convolutional seq2seq', '[BOLD] Base + fluency boost learning']
We further study the effectiveness of fluency boost learning and inference for different error types. One can see that fluency boost learning improves recall for most error types, demonstrating that fluency boost learning approach can generate sentences with diverse errors to help training.