## 7 Results
### Simultaneous Diacritics and Typos Corrections  
The results of the simultaneous diacritic and typographic error corrections are represented in Table 5. We see that the alpha-word accuracy results are significantly lower across the board, compared to restoring the diacritics alone.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Language**} & \multicolumn{3}{c}{**One Dictionary Candidate**} & \multicolumn{3}{c}{**Two Dictionary Candidates**} \\ \cline{2-7}  & **\#samples:\(\times\)0.5** & \(\times\)**1** & \(\times\)**8** & \(\times\)**0.5** & \(\times\)**1** & \(\times\)**8** \\ \hline Croatian & 6.37 \(\pm\) 0.98 & 4.98 \(\pm\) 0.52 & & 1.18 \(\pm\) 0.03 & 1.01 \(\pm\) 0.06 & \\ Czech & 4.74 \(\pm\) 0.19 & 3.53 \(\pm\) 0.03 & & 0.45 \(\pm\) 0.02 & 0.37 \(\pm\) 0.01 & \\ French & 5.29 \(\pm\) 0.17 & 4.98 \(\pm\) 0.48 & & 0.31 \(\pm\) 0.02 & 0.27 \(\pm\) 0.01 & \\ Hungarian & 7.35 \(\pm\) 0.48 & 4.37 \(\pm\) 0.10 & 1.42 & 0.84 \(\pm\) 0.03 & 0.62 \(\pm\) 0.00 & 0.34 \\ Irish & 2.13 \(\pm\) 0.21 & 2.27 \(\pm\) 0.84 & & 0.56 \(\pm\) 0.03 & 0.57 \(\pm\) 0.08 & \\ Latvian & 2.43 \(\pm\) 0.17 & 1.77 \(\pm\) 0.11 & 0.69 & 0.43 \(\pm\) 0.00 & 0.37 \(\pm\) 0.02 & 0.23 \\ Lithuanian & 2.61 \(\pm\) 0.18 & 2.00 \(\pm\) 0.36 & 0.58 & 0.34 \(\pm\) 0.02 & 0.27 \(\pm\) 0.01 & 0.12 \\ Polish & 4.06 \(\pm\) 0.24 & 2.56 \(\pm\) 0.15 & & 0.30 \(\pm\) 0.03 & 0.24 \(\pm\) 0.01 & \\ Romanian & 3.66 \(\pm\) 0.44 & 2.63 \(\pm\) 0.12 & & 0.82 \(\pm\) 0.02 & 0.63 \(\pm\) 0.02 & \\ Slovak & 4.29 \(\pm\) 0.03 & 3.00 \(\pm\) 0.21 & & 0.54 \(\pm\) 0.02 & 0.44 \(\pm\) 0.01 & \\ Spanish & 5.4 \(\pm\) 0.54 & 4.18 \(\pm\) 0.57 & & 0.95 \(\pm\) 0.04 & 0.82 \(\pm\) 0.03 & \\ Turkish & 10.5 \(\pm\) 10.84 & 2.70 \(\pm\) 0.24 & & 3.83 \(\pm\) 4.68 & 1.01 \(\pm\) 0.03 & \\ Vietnamese & 2.6 \(\pm\) 0.10 & 2.38 \(\pm\) 0.20 & 1.31 & 1.52 \(\pm\) 0.16 & 1.21 \(\pm\) 0.04 & 0.32 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Alpha-word error ratio between the ByT5 and Dictionary methods for two word groups and models in different training stages. The values higher than 1 indicate that the Dictionary method restores diacritics better. The first word group corresponds to words with exactly one possible translation target, and the second word group corresponds to words with two translation targets. Groups are determined by the training set statistics, while results are reported on the testing set.  
We also added correction results that were obtained with the open-source Hungpell spellchecker [52] by replacing the words that it found to be incorrectly spelled with its first suggestion. The results indicate that it is barely better than raw uncorrected sentences. It is also significantly worse than our Dictionary approach, which is specialized in restoring diacritics.  
The Dictionary method was used in the same way as the previous experiment, i.e., it was \"trained\" on the typo-free diacritization-only task in both the standalone and hybrid approaches.  
The reduction of accuracy the ByT5 model, on average, is by 7.84%, while for hybrid Dict.+ByT5 approach, it was 3.71%. A smaller reduction for the hybrid method suggests that the transformers do not cope well with the same words that it successfully dealt with when there were no typos present. A possible reason may be that more learning is required by both tasks, and up to 10 h of training might not be enough. Training the Hungarian model up to 19 times longer improves the performance substantially, but the gap of 2.98% between the ByT5 model and the hybrid remains.