Previous research in GEC has highlighted that performance is associated with the model's error detection and error correction ability . They concluded that their transformer model was able to generalize error detection to a real-world evaluation whereas correction is more difficult. In our case, the drop in performance could be partly explained by bad error detection illustrated by the lower percentage of inputs that had been altered by the translator. Interestingly, for the VAE the percentage of altered inputs was 90 % but the validation rate was still low, indicating that finding the right correction is challenging for the model. In general, the relatively high percentage of unaltered