## 5 Experimental Results
The table does not contain the results of the case without any normalization as it turns out that the proposed object 2D normalization method is superior to the other normalization methods we considered. Especially, when we trained our neural network with the keypoint feature vector which was obtained by simply concatenating the \(x\)- and \(y\)-coordinates of keypoints without any normalization, the validation loss never decreased. While any kind of normalization seemed to work positively, it is quite interesting to see that there is an additional boost in translation performance when the object-wise normalization and the 2D normalization were used together. **Effect of attention mechanisms. ** Here, we compared four types of encoder-decoder architectures that are specialized in various machine translation tasks. Table 6 demonstrates the clear contrast between the attention-based model by [PERSON] et al. [41] and the Transformer [42]. While the model of [PERSON] et al. shows better performance than the Transformer on the validation set that contains more similar data to the training set, the Transformer generalizes much better to the test set, which consists of sign videos of an independent signer. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Attention Type**} & \multicolumn{5}{c}{**Validation Set**} & \multicolumn{5}{c}{**Test Set**} \\ \cline{2-9}  & **ROUGE-L** & **METEOR** & **BLEU** & **CIDEr** & **ROUGE-L** & **METEOR** & **BLEU** & **CIDEr** \\ \hline Vanilla seq2 seq [7] & 90.03 & 62.66 & 87.79 & 3.838 & 62.93 & 38.03 & 50.80 & 2.129 \\ [PERSON] et al. [40] & 94.72 & 67.44 & 94.03 & 4.264 & 71.45 & 44.06 & 63.38 & 2.616 \\ [PERSON] et al. [41] & **96.63** & **72.24** & **95.86** & **4.322** & **73.61** & 46.52 & 65.26 & 2.794 \\ Transformer [42] & 94.14 & 69.27 & 92.90 & 4.227 & 73.18 & **47.03** & **66.58** & **2.857** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance comparison of sign language translation on different types of attention mechanisms. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Method** & **ROUGE-L** & **METEOR** & **BLEU** & **CIDEr** \\ \hline Feature Normalization & 66.28 & 40.94 & 56.91 & 2.401 \\
2D Normalization & 72.05 & 44.98 & 62.69 & 2.706 \\ Object Normalization & 64.16 & 38.84 & 53.83 & 2.235 \\ Object 2D Normalization & **73.61** & **46.52** & **65.26** & **2.794** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Effect of different feature normalization methods on the translation performance. The results were obtained on the test set. Effect of augmentation factor.We examined the effect of data augmentation by random frame skip sampling and summarize the experimental results in Table 7. We call the number of training samples randomly sampled from a single sign video the _augmentation factor_. Since the number of sign videos in the training set is 9432, the total number of training samples after the data augmentation is 943,200 when the augmentation factor is 100. It should be noted that we do not include the result when we do not augment data by random frame sampling because the validation loss did not decrease at all due to severe overfitting. The result shows that the optimal augmentation factor is 50 for the validation and test set. This implies that a larger augmentation factor does not always lead to improvement in performance and even in generalization capability. Effect of the number of sampled frames.It is useful to know the optimal number of frames if we plan to develop a real-time sign language translation system because we can reduce the computational cost of the inference engine by efficiently skipping unnecessary frames. Table 8 shows how the number of sampled frames affects the translation performance. As the sequence-to-sequence model works for any variable-length input sequences, we do not necessarily fix the number of sampled frames.