# Enhance Text-to-Text Transfer Transformer with Generated Questions for Thai Question Answering
## 5 Results
### Comparison of the Syllable-Level F1  
From Tables 4 and 5, the syllable-level F1 scores of all combinations are greater than the word-level F1 score. The mT5-SEQ-FLT in Thai Wiki QA has a syllable-level F1 score greater than the word-level F1 score by 1.65%, and the mT5-SEQ-ALL in iApp Wiki QA has a syllable-level F1 score greater than the word-level F1 score by 1.18%. The reason is that there are some predicted answers and/or ground truth answers that the word tokenizer used in the F1 score calculation does not perform correctly due to inability of tokenizer itself. This results in some overlapping words not counted to calculate the F1 score. However, the syllable tokenizer can tokenize those overlapping words into syllables and is able to calculate a nearer-correct F1 score. We provide some examples of the predicted answer and ground truth answer in Table 7, showing that the syllable F1 score gives a more accurate result than the word-level F1 score.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Question Type** & \multicolumn{2}{c}{**mT5 Baseline**} & \multicolumn{3}{c}{**mT5-SEQ-ALL**} & \multicolumn{3}{c}{**\% of Improvement**} \\ \cline{2-9}  & **EM** & **W-F1** & **S-F1** & **EM** & **W-F1** & **S-F1** & **EM** & **W-F1** & **S-F1** \\ \hline Overall & & & & & & & & \\ Performance & & & & & & & & \\ \hline Who & 69.16 & 82.03 & 81.40 & 73.05 & 85.87 & 85.30 & 5.62 & 4.68 & 4.79 \\ What & 62.90 & 77.45 & 80.48 & 68.82 & 82.89 & 85.24 & 9.41 & 7.02 & 5.91 \\ Where & 67.54 & 83.23 & 83.02 & 76.61 & 88.82 & 88.93 & 13.42 & 6.72 & 7.12 \\ Year & 76.04 & 83.06 & 85.03 & 83.83 & 88.75 & 89.53 & 10.24 & 6.85 & 5.29 \\ Date & 58.88 & 79.53 & 79.21 & 72.08 & 90.13 & 89.43 & 22.42 & 13.33 & 12.90 \\ Number & 61.99 & 74.14 & 76.67 & 68.19 & 78.94 & 81.32 & 10.00 & 6.47 & 6.07 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Model performance evaluated on the iApp Wiki QA dataset classified by question types.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Who** & **What** & **Where** & **Year** & **Date** & **Number** \\ \hline _11.7_ & _2.5_ & _2.5_ & _2.5_ & _2.5_ & _2.5_ & _2.5_ \\ (Who) & (What) & (Where) & (What year) & (Which date) & (How many/much) \\ _11.7_ & _2.5_ & _2.5_ & _2.5_ & _2.5_ & _2.5_ \\ (Which one) & (What/Which...) & (Where) & (What year) & (Which date) & (How many/much) \\ _11.7_ & _2.5_ & _2.5_ & _2.5_ & _2.5_ & _2.5_ \\ (Which one) & (What/Which...) & (Which country) & (What B.E. year) & (When) & (How many/much) \\  & & & _2.5_ & _2.5_ & _2.5_ & _2.5_ \\  & & (Which province) & (What C.E. year) & & & \\ \hline \hline \end{tabular}
\end{table}
Table 8: Example of question word categories in Thai.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & \begin{tabular}{c} **‘Newmm’** \\ **(Word-Level F1)** \\ \end{tabular} & \
\begin{tabular}{c} **AttaCut** \\ **(Word-Level F1)** \\ \end{tabular} &
\begin{tabular}{c} **Syllable Tokenizer** \\ **(Syllable-Level F1)** \\ \end{tabular} \\ \hline WangchanBERTa & 70.73 & 64.46 & 74.71 \\ Baseline & 78.24 & 73.58 & 80.35 \\ mT5-SEQ-FLT & 83.64 & 78.98 & 85.29 \\ \hline \hline \end{tabular}
\end{table}
Table 11: The result of Word-level F1 with different types of tokenizers of the Thai Wiki QA dataset.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Question Type** & \multicolumn{2}{c}{**mT5 Baseline**} & \multicolumn{3}{c}{**mT5-SEQ-FLT**} & \multicolumn{3}{c}{**\% of Improvement**} \\ \cline{2-10}  & **EM** & **W-F1** & **S-F1** & **EM** & **W-F1** & **S-F1** & **EM** & **W-F1** & **S-F1** \\ \hline Overall & & & & & & & & \\ Performance & 64.14 & 78.24 & 80.35 & 70.42 & 83.64 & 85.29 & 9.79 & 6.90 & 6.15 \\ \hline Who & 69.16 & 82.03 & 81.40 & 73.05 &