A Nested Attention Neural Hybrid Model for Grammatical Error Correction - Gao, Jianfeng (ACL 2017, 2017)
Grammatical error correction (GEC) systems strive to correct both global errors in word order and usage, and local errors in spelling and inﬂection. Further developing upon recent work on neural machine translation, we propose a new hybrid neural model with nested attention layers for GEC. Experiments show that the new model can effectively correct errors of both types by incorporating word and character-level information, and that the model signiﬁcantly outperforms previous neural models for GEC as measured on the standard CoNLL14 benchmark dataset. Further analysis also shows that the superiority of the proposed model can be largely attributed to the use of the nested attention mechanism, which has proven particularly effective in correcting local errors that involve small edits in orthography.