Paper: Joint Learning and Inference for Grammatical Error Correction

ACL ID D13-1074
Title Joint Learning and Inference for Grammatical Error Correction
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2013

State-of-the-art systems for grammatical er- ror correction are based on a collection of independently-trained models for specific er- rors. Such models ignore linguistic interac- tions at the sentence level and thus do poorly on mistakes that involve grammatical depen- dencies among several words. In this paper, we identify linguistic structures with interact- ing grammatical properties and propose to ad- dress such dependencies via joint inference and joint learning. We show that it is possible to identify interac- tions well enough to facilitate a joint approach and, consequently, that joint methods correct incoherent predictions that independently- trained classifiers tend to produce. Further- more, because the joint learning model con- siders interacting phenomena during training, it ...