  Recent works in Grammatical Error Correction (GEC) have leveraged the
progress in Neural Machine Translation (NMT), to learn rewrites from parallel
corpora of grammatically incorrect and corrected sentences, achieving
state-of-the-art results. At the same time, Generative Adversarial Networks
(GANs) have been successful in generating realistic texts across many different
tasks by learning to directly minimize the difference between human-generated
and synthetic text. In this work, we present an adversarial learning approach
to GEC, using the generator-discriminator framework. The generator is a
Transformer model, trained to produce grammatically correct sentences given
grammatically incorrect ones. The discriminator is a sentence-pair
classification model, trained to judge a given pair of grammatically
incorrect-correct sentences on the quality of grammatical correction. We
pre-train both the discriminator and the generator on parallel texts and then
fine-tune them further using a policy gradient method that assigns high rewards
to sentences which could be true corrections of the grammatically incorrect
text. Experimental results on FCE, CoNLL-14, and BEA-19 datasets show that
Adversarial-GEC can achieve competitive GEC quality compared to NMT-based
baselines.
