Joint learning and inference for grammatical error correction

State-of-the-art systems for grammatical error correction are based on a collection of independently-trained models for specific errors. Such models ignore linguistic interactions at the sentence level and thus do poorly on mistakes that involve grammatical dependencies among several words. In this paper, we identify linguistic structures with interacting grammatical properties and propose to address such dependencies via joint inference and joint learning. We show that it is possible to identify interactions well enough to facilitate a joint approach and, consequently, that joint methods correct incoherent predictions that independently-trained classifiers tend to produce. Furthermore, because the joint learning model considers interacting phenomena during training, it is able to identify mistakes that require making multiple changes simultaneously and that standard approaches miss. Overall, our model significantly outperforms the Illinois system that placed first in the CoNLL-2013 shared task on grammatical error correction.