A Self-Refinement Strategy for Noise Reduction in Grammatical Error Correction
A Self-Refinement Strategy for Noise Reduction in Grammatical Error Correction
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DOI：
10.18653/v1/2020.findings-emnlp.26
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发表时间：
2020-10
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影响因子：
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通讯作者：
Masato Mita;Shun Kiyono;Masahiro Kaneko;Jun Suzuki;Kentaro Inui
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作者：
Masato Mita;Shun Kiyono;Masahiro Kaneko;Jun Suzuki;Kentaro Inui
现有的语法错误纠正 (GEC) 方法很大程度上依赖于手动创建的 GEC 数据集的监督学习。然而，很少有人关注验证和确保数据集的质量，以及低质量数据如何影响 GEC 性能。我们确实发现存在不可忽视的“噪音”，错误被不恰当地编辑或未被纠正。为了解决这个问题，我们设计了一种自我细化方法，其关键思想是利用现有模型的预测一致性对这些数据集进行去噪，并且性能优于强去噪基线方法。我们进一步应用了特定于任务的技术，并在 CoNLL-2014、JFLEG 和 BEA-2019 基准测试中实现了最先进的性能。然后，我们分析了所提出的去噪方法的效果，发现我们的方法可以提高校正的覆盖范围并促进流畅的编辑，这反映在更高的召回率和整体性能上。
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.