Leveraging Denoised Abstract Meaning Representation for Grammatical Error Correction

• URL: http://arxiv.org/abs/2307.02127v1
• Date: Wed, 5 Jul 2023 09:06:56 GMT
• Title: Leveraging Denoised Abstract Meaning Representation for Grammatical Error Correction
• Authors: Hejing Cao and Dongyan Zhao
• Abstract summary: Grammatical Error Correction (GEC) is the task of correcting errorful sentences into grammatically correct, semantically consistent, and coherent sentences. We propose the AMR-GEC, a seq-to-seq model that incorporates denoised AMR as additional knowledge.
• Score: 53.55440811942249
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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