Stronger Baselines for Grammatical Error Correction Using Pretrained Encoder–Decoder Model
Table 3: German, Czech, and Russian GEC results. These models are not an ensemble of multiple models.
['[EMPTY]', 'Náplava and Straka ( 2019 )', 'P 78.21', 'R 59.94', 'F0.5 73.31']
German, Czech, and Russian. ’s model. This could be because the training data for Russian GEC are scarce compared to those of German or Czech. To investigate the effect of corpus size, we additionally trained mBART model with a 10M pseudo corpus, using the Grundkiewicz et al. ’s method, and fine-tuned it with the learner corpus to compensate the low-resource scenario.