Automatic Grammatical Error Correction Based on Edit Operations Information

Abstract

For second language learners, a reliable and effective Grammatical Error Correction (GEC) system is imperative, since it can be used as an auxiliary assistant for errors correction and helps learners improve their writing ability. Researchers have paid more emphasis on this task with deep learning methods. Better results were achieved on the standard benchmark datasets compared to traditional rule based approaches. We treat GEC as a special translation problem which translates wrong sentences into correct ones like other former works. In this paper, we propose a novel correction system based on sequence to sequence (Seq2Seq) architecture with residual connection and semantically conditioned LSTM (SC-LSTM), incorporating edit operations as special semantic information. Our model further improves the performance of neural machine translation model for GEC and achieves state-of-the-art \(F _{0.5}\)-score on standard test data named CoNLL-2014 compared with other methods that without any re-rank approach.