## 5 Experimental Results
However, it is useful to know the optimal number of frames as the translation performance of the sequence-to-sequence models tends to decline with longer input sequences due to the vanishing gradient problem [52]. Our experimental result shows that the optimal number of frames for the best translation performance is 40 for the validation set and 50 for the test set. Effect of batch size.Recently, it is increasingly accepted that training with a small batch often generalizes better to the test set than training with large batch [53; 54]. However, our experimental results provided in Table 9 show the opposite phenomenon. We suspect that this is due to the scale of the original dataset because the large batch is known to be useful to prevent overfitting to some extent. \begin{table}
\begin{tabular}{r r r r r r r r r} \hline \hline \multirow{2}{*}{**Augmentation Factor**} & \multicolumn{4}{c}{**Validation Set**} & \multicolumn{4}{c}{**Test Set**} \\ \cline{2-9}  & **ROUGE-L** & **METEOR** & **BLEU** & **CIDEr** & **ROUGE-L** & **METEOR** & **BLEU** & **CIDEr** \\ \hline
128 & 96.63 & 72.24 & 95.86 & 4.322 & 73.61 & 46.52 & **65.26** & 2.794 \\
64 & **96.94** & **73.20** & **96.35** & **4.332** & 72.93 & 46.06 & 63.91 & 2.725 \\
32 & 95.65 & 70.68 & 94.57 & 4.231 & 71.94 & 45.30 & 62.71 & 2.673 \\
16 & 93.74 & 67.58 & 92.74 & 4.118 & 70.63 & 43.86 & 61.94 & 2.571 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Effects of the batch size on sign language translation performance. \begin{table}
\begin{tabular}{r r r r r r r r r} \hline \hline \multirow{2}{*}{**Number of Frames**} & \multicolumn{4}{c}{**Validation Set**} & \multicolumn{4}{c}{**Test Set**} \\ \cline{2-9}  & **ROUGE-L** & **METEOR** & **BLEU** & **CIDEr** & **ROUGE-L** & **METEOR** & **BLEU** & **CIDEr** \\ \hline
50 & **96.63** & 72.24 & 95.86 & 4.322 & **73.61** & **46.52** & **65.26** & **2.794** \\
40 & 96.52 & **72.36** & **95.96** & **4.327** & 72.71 & 46.18 & 64.35 & 2.757 \\
30 & 95.88 & 70.60 & 94.87 & 4.281 & 73.35 & 46.46 & 64.48 & 2.778 \\
20 & 94.38 & 68.40 & 92.98 & 4.181 & 72.37 & 45.37 & 62.19 & 2.693 \\
10 & 83.26 & 55.81 & 78.43 & 3.427 & 65.89 & 40.65 & 54.01 & 2.308 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Effects of the number of sampled frames on sign language translation performance. Generalizations to real world data.As we can see in Figure 2, the sign videos in our dataset were recorded in a clear background. This led us to investigate the performance of our model when the background area of sign videos was cluttered. Moreover, we also checked how well our system generalizes to the real world situations by testing the system against more realistic sign video examples that were collected in a much less constrained setting. We collected 30 additional sign videos of five signs from six novice signers in relatively cluttered background to test the generalization ability of our system. Note that we selected relatively easier five signs among the 105 signs since it is very difficult to follow complicated signs for the novice singers who never learned signs before. Figure 5 contains example frames from the additional sign videos. As a result, our system achieved 89.06 (ROUGE-L), 60.64 (METEOR), 77.08 (BLEU) and 2.860 (CIDEr) in the four metrics. The experimental result shows that our system generalizes well to the real world conditions such as cluttered background area and clumsy signs from non-experts.