Leveraging Denoised Abstract Meaning Representation for Grammatical
  Error Correction
Hejing Cao Dongyan Zhao
Grammatical Error Correction (GEC) is the task of correcting errorful
sentences into grammatically correct, semantically consistent, and coherent
sentences. Popular GEC models either use large-scale synthetic corpora or use a
large number of human-designed rules. The former is costly to train, while the
latter requires quite a lot of human expertise. In recent years, AMR, a
semantic representation framework, has been widely used by many natural
language tasks due to its completeness and flexibility. A non-negligible
concern is that AMRs of grammatically incorrect sentences may not be exactly
reliable. In this paper, we propose the AMR-GEC, a seq-to-seq model that
incorporates denoised AMR as additional knowledge. Specifically, We design a
semantic aggregated GEC model and explore denoising methods to get AMRs more
reliable. Experiments on the BEA-2019 shared task and the CoNLL-2014 shared
task have shown that AMR-GEC performs comparably to a set of strong baselines
with a large number of synthetic data. Compared with the T5 model with
synthetic data, AMR-GEC can reduce the training time by 32\% while inference
time is comparable. To the best of our knowledge, we are the first to
incorporate AMR for grammatical error correction.