A Self-Refinement Strategy for Noise Reduction in Grammatical Error Correction

• URL: http://arxiv.org/abs/2010.03155v1
• Date: Wed, 7 Oct 2020 04:45:09 GMT
• Title: A Self-Refinement Strategy for Noise Reduction in Grammatical Error Correction
• Authors: Masato Mita, Shun Kiyono, Masahiro Kaneko, Jun Suzuki and Kentaro Inui
• Abstract summary: Existing approaches for grammatical error correction (GEC) rely on supervised learning with manually created GEC datasets. There is a non-negligible amount of "noise" where errors were inappropriately edited or left uncorrected. We propose a self-refinement method where the key idea is to denoise these datasets by leveraging the prediction consistency of existing models.
• Score: 54.569707226277735
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing approaches for grammatical error correction (GEC) largely rely on supervised learning with manually created GEC datasets. However, there has been little focus on verifying and ensuring the quality of the datasets, and on how lower-quality data might affect GEC performance. We indeed found that there is a non-negligible amount of "noise" where errors were inappropriately edited or left uncorrected. To address this, we designed a self-refinement method where the key idea is to denoise these datasets by leveraging the prediction consistency of existing models, and outperformed strong denoising baseline methods. We further applied task-specific techniques and achieved state-of-the-art performance on the CoNLL-2014, JFLEG, and BEA-2019 benchmarks. We then analyzed the effect of the proposed denoising method, and found that our approach leads to improved coverage of corrections and facilitated fluency edits which are reflected in higher recall and overall performance.

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