Quality estimation models have been developed to assess the corrections made by grammatical error correction (GEC) models when the reference or gold-standard corrections are not available. An ideal quality estimator can be utilized to combine the outputs of multiple GEC systems by choosing the best subset of edits from the union of all edits proposed by the GEC base systems. However, we found that existing GEC quality estimation models are not good enough in differentiating good corrections from bad ones, resulting in a low F0.5 score when used for system combination. In this paper, we propose GRECO, a new state-of-the-art quality estimation model that gives a better estimate of the quality of a corrected sentence, as indicated by having a higher correlation to the F0.5 score of a corrected sentence. It results in a combined GEC system with a higher F0.5 score. We also propose three methods for utilizing GEC quality estimation models for system combination with varying generality: model-agnostic, model-agnostic with voting bias, and model-dependent method. The combined GEC system outperforms the state of the art on the CoNLL-2014 test set and the BEA-2019 test set, achieving the highest F0.5 scores published to date.

我们提出GRECO，一种新的最先进的质量估计模型，用于评估纠正后句子的质量，得到与F0.5分数更高的更好估计，从而实现更高的F0.5分数的综合GEC系统。我们还提出了三种利用GEC质量估计模型进行系统组合的方法，包括模型无关方法、带有投票偏差的模型无关方法和模型相关方法。综合GEC系统在CoNLL-2014测试集和BEA-2019测试集上表现超过现有最新水平，达到迄今最高的F0.5分数。

通过质量估计进行语法错误校正的系统组合