# Neural Network-Based Bilingual Lexicon Induction for Indonesian Ethnic Languages
## 4 Results
### Neural Network Performances for Other Ethnic Languages
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{6}{c}{**K-Fold Cross-Validation Indonesian–Malay**} \\ \cline{2-7}  & **K = 1** & **K = 2** & **K = 3** & **K = 4** & **K = 5** & **Average Precision** \\ \hline Bi-LSTM (encoder), LSTM (decoder) & 63.82 & 62.45 & 63.23 & 60.29 & 62.84 & 62.52 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Evaluation of the character-level model in Indonesian–Javanese. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{6}{c}{**K-Fold Cross-Validation Indonesian–Javanese**} \\ \cline{2-7}  & **K = 1** & **K = 2** & **K = 3** & **K = 4** & **K = 5** & **Average Precision** \\ \hline Bi-LSTM (encoder), LSTM (decoder) & 62.02 & 59.30 & 59.62 & 55.34 & 61.08 & 59.69 \\ \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 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. network-bilingual lexicon induction model can be improved by increasing the size of the bilingual dictionaries so that the model can learn more translation patterns in Minangkabau, Malay, Palembang, Javanese, Sundanese, and other Indonesian ethnic languages. Conceptualization, Y.M. and K.R. ; methodology, Y.M. and K.R. ; software, A.H.N. ; validation, Y.M. and A.H.N. ; formal analysis, Y.M. and A.H.N. ; investigation, Y.M. and A.H.N. ; resources, K.R. ; data curation, K.R. and A.H.N. ; writing--original draft preparation, K.R. ; writing--review and editing, A.H.N. ; visualization, K.R. and A.H.N. ; supervision, Y.M. ; funding acquisition, Y.M. All authors have read and agreed to the published version of the manuscript. This research was partially supported by a Grant-in-Aid for Scientific Research (B) (21H03561, 2021-2024) and a Grant-in-Aid Young Scientists (A) (17H04706, 2017-2020) from the Japan Society for the Promotion of Science (JSPS). 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 authors declare no conflict of interest. The following abbreviations are used in this manuscript: \begin{tabular}{l l} ASJP & Automated Similarity Judgment Program \\ Bi-LSTM & bidirectional long short-term memory \\ BPE & byte pair encoding \\ LSTM & long short-term memory \\ RNN & recurrent neural network \\ seq2 seq & sequence-to-sequence \\ \end{tabular}