# Best Practices of Convolutional Neural Networks for Question Classification
## 3 Results and Discussion
### CNN Architecture  
The CNN architecture was analyzed firstly in terms of both the filter size (7) and their total number (8), and then with regard to the activation function involved in (10), while their interactions were neglected. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline _De_ & **10** & **100** & **300** & **500** \\ \hline _Acc (\%)_ & **English** & 54.8 & 74.0 & 77.8 & 78.1 \\
**Italian** & 41.0 & 73.7 & 76.1 & 75.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Testing accuracy obtained by different embedding dimensions 1. Figure 3: Testing accuracy obtained by different embedding dimensions, for English (**red**) and Italian (**blue**). Different filter sizes were experimented in the range [1; 10], taking into account all possible sizes. The functions fitted in this whole range to predict accuracy reveal no significant linear or quadratic effect; however, in the restrictions of this range, the experimental results showed significant trends, associated with significant improvements in correspondence of individual filter sizes with respect to the others, as discussed in the following. Firstly, all filters with the same size were employed. Results for both languages are reported in Table 8 and shown in Figure 4.  
Within the first set of experiments, the best single filter size results \(F\)s = 2, which corresponds to significant improvements with respect to both \(F\)s = 1 and \(F\)s > 2. The trend was similar for both languages, while a misalignment results for \(F\)s = 8, which may be due to experimental variance. Then, in order to evaluate the possibility of using filters of different sizes at the same time, as suggested by previous works [19; 20], 150 filters of size 2 were fixed, while the size of the other 150 was varied in the same interval. Results for both languages are reported in Table 9 and shown in Figure 5.  
These results again allow to individuate \(F\)s = 2 as the best filter size, also in association with other filters of size 2. This corresponds to significant improvements with respect to \(F\)s = 1 and slight improvements with respect to \(F\)s > 2. Figure 4: Testing accuracy obtained by different filter sizes, for English (**red**) and Italian (**blue**). \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{\(F\)s} & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** \\ \hline \multirow{2}{*}{_Acc (\%)_} & **English** & 79.0 & 81.8 & 79.4 & 79.2 & 78.9 & 78.3 & 78.6 & 79.7 & 78.3 & 78.0 \\  & **Italian** & 78.6 & 81.0 & 79.3 & 78.3 & 79.3 & 77.6 & 78.1 & 76.5 & 77.2 & 77.5 \\ \hline \hline \end{tabular}  
* Other settings were: _AvoidPunctuation_ = True, _WEinit_ = pre-trained, _De_ = 300, _WEtuning_ = dynamic, _Not_ = 300, \(f\) = ReLU, _batch_ = 900, _P_\({}_{\text{\tiny{Lasp}}}\) = 0.5, _l2_ = 3.0, _optimizer_ = Adadelta, \(\eta\) = 0.1.  
\end{table} 
Table 8: Testing accuracy obtained by different filter sizes 1. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{\(F\)s} & **2,1** & **2,2** & **2,3** & **2,4** & **2,5** & **2,6** & **2,7** & **2,8** & **2,9** & **2,10** \\ \hline \multirow{2}{*}{_Acc (\%)_} & **English** & 79.9 & 81.8 & 80.9 & 79.8 & 81.2 & 80.8 & 80.4 & 80.1 & 79.9 & 80.7 \\  & **Italian** & 79.4 & 81.0 & 80.6 & 80.1 & 79.2 & 79.0 & 80.3 & 78.9 & 78.3 & 79.4 \\ \hline \hline \end{tabular}  
* Other settings were: _AvoidPunctuation_ = True, _WEinit_ = pre-trained, _De_ = 300, _WEtuning_ = dynamic, _Not_ = 300, \(f\) = ReLU, _batch_ = 900, _P_\({}_{\text{\tiny{Lasp}}}\) = 0.5, _l2_ = 3.0, _optimizer_ = Adadelta, \(\eta\) = 0.1.  
\end{table} 
Table 9: Testing accuracy obtained by different filter sizes 1. These results mean that substituting 150 filters with others having different sizes does not improve the accuracy. Therefore, further experiments were performed by fixing 200 filters of size 2, while the size of only 100 varies.