Neural sequence-to-sequence (seq2seq) approaches have proven to be successful in grammatical error correction (GEC). Based on the seq2seq framework, we propose a novel fluency boost learning and inference mechanism. Fluency boosting learning generates diverse error-corrected sentence pairs during training, enabling the error correction model to learn how to improve a sentence's fluency from more instances, while fluency boosting inference allows the model to correct a sentence incrementally with multiple inference steps. Combining fluency boost learning and inference with convolutional seq2seq models, our approach achieves the state-of-the-art performance: 75.72 (F_{0.5}) on CoNLL-2014 10 annotation dataset and 62.42 (GLEU) on JFLEG test set respectively, becoming the first GEC system that reaches human-level performance (72.58 for CoNLL and 62.37 for JFLEG) on both of the benchmarks.
@article{arxiv.1807.01270,
title = {Reaching Human-level Performance in Automatic Grammatical Error Correction: An Empirical Study},
author = {Tao Ge and Furu Wei and Ming Zhou},
journal= {arXiv preprint arXiv:1807.01270},
year = {2018}
}
Comments
Substantial text overlap with "Fluency Boost Learning and Inference for Neural Grammatical Error Correction" (accepted by ACL 2018)