# Neural Network-Based Bilingual Lexicon Induction for Indonesian Ethnic Languages
## 4 Results
### Neural Network Performances for Other Ethnic Languages  
The best neural network model, which is the character-level model with Bi-LSTM as the encoder and LSTM as the decoder, was utilized in additional experiments to create Indonesian-Malay, Indonesian-Palembang, Indonesian-Javanese, and Indonesian-Sundanese bilingual dictionaries. Unfortunately, we only have small secondary data obtained from [PERSON] et al. [2], as shown in Table 9. The source code and the bilingual dictionaries are available online ([[https://github.com/arbihazanst/neural-network-lexicon-induction](https://github.com/arbihazanst/neural-network-lexicon-induction)]([https://github.com/arbihazanst/neural-network-lexicon-induction](https://github.com/arbihazanst/neural-network-lexicon-induction)), accessed date: 24 July 2023). The Indonesian-Malay dictionary has 5229 translation pairs, divided into 80% in the training set (4183), and 20% in the test set (1046); the number of tokens in Indonesia is 27 characters, and in Malay is 30 characters. Table 10 shows the performance results of the character-level model for the Indonesian-Malay experiment. The Indonesian-Palembang dictionary has 5098 translation pairs, divided into 80% in the training set (4078), and 20% in the test set (1020); the number of tokens in Indonesia is 28 characters, and in Palembang is 29 characters. Table 11 shows the performance results of the character-level model of the Indonesian-Palembang experiment. The Indonesian-Javanese dictionary has 4778 translation pairs, divided into 80% in the training set (3822) and 20% in the test set (956); the number of tokens in Indonesia is 27 characters, and in Javanese is 32 characters. Table 12 shows the performance results of the character-level model of the Indonesian-Javanese experiment. Finally, the Indonesian-Sundanese dictionary has 5045 translation pairs, divided into 80% in the training set (4036) and 20% in the test set (1009); the number of tokens in Indonesia is 26 characters, and in Sundanese is 33 characters. Table 13 shows the performance results of the character-level model for the Indonesian-Sundanese experiment. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Result** & **Neutal** & \\  & **Correct** & **Wrong** \\ \hline Rule & Correct & 414 & 61 \\  & Wrong & 603 & 184 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparisons between the neural network approach and rule-based approach. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Language Pair** & **\#Translation Pair** & **\#Training Set** & **\#Test Set** \\ \hline Indonesian–Minangkabau & 13,761 & 11,008 & 2753 \\ Indonesian–Malay & 5229 & 4183 & 1046 \\ Indonesian–Palembang & 5098 & 4078 & 1020 \\ Indonesian–Javanese & 4778 & 3822 & 956 \\ Indonesian–Sundanese & 5045 & 4036 & 1009 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Dataset for all ethnic languages. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{6}{c}{**K-Fold Cross-Validation Indonesian–Sundanese**} \\ \cline{2-7}  & **K = 1** & **K = 2** & **K = 3** & **K = 4** & **K = 5** & **Average Precision** \\ \hline Bi-LSTM (encoder), LSTM (decoder) & 57.77 & 58.47 & 59.36 & 59.26 & 57.48 & 58.46 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Evaluation of the character-level model in Indonesian–Sundanese. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{6}{c}{**K-Fold Cross-Validation Indonesian–Sundanese**} \\ \cline{2-7}  & **K = 1** & **K = 2** & **K = 3** & **K = 4** & **K = 5** & **Average Precision** \\ \hline Bi-LSTM (encoder), LSTM (decoder) & 64.72 & 66.15 & 65.20 & 65.96 & 63.38 & 65.08 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Evaluation of the character-level model in Indonesian–Malay.