# Low-Resource Neural Machine Translation Improvement Using Source-Side Monolingual Data
## 5 Methodology
### Experimental Architecture
#### 5.4.2 Self-Learning
Tables 6 and 7 show the BLEU, chrF, and TER scores of the self-trained NMT models trained on a combination of the authentic and synthetic parallel datasets for Wolaytta-English and English-Wolaytta translation, respectively. ### Fine-Tuning

As shown in Section 5.4.3, to train the final NMT model, we fine-tuned the self-trained NMT models in both in-domain and mixed validation sets using the authentic parallel datasets. Tables 8 and 9 present the results of fine-tuning on the authentic parallel dataset for Wolaytta-English and English-Wolaytta translations, respectively. ## 7 Discussion

In this section, we discuss the performance of our NMT models. In Section 7.1, we consider the performance of the three baseline NMT models followed by a discussion of the performance of the self-trained NMT models in Section 7.2. Finally, in Section 7.3, we discuss the performance of our final NMT model. To evaluate the performance of our NMT models, we used BLEU, chrF, and TER automatic evaluation metrics. Higher BLEU and \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model (English–Wolaytta)** & **BLEU (\%)** & **chrF** & **TER** & **Loss** \\ \hline Transformer (mixed validation set) & 14.7 & 34.7 & 78.1 & 3.72 \\ Transformer (in-domain validation) & **14.9** & **35.1** & **77.6** & **3.61** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: BLEU, chrF, and TER scores of NMT models trained on the combination of synthetic and authentic datasets for English–Wolaytta translation. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model (English–Wolaytta)** & **BLEU (\%)** & **chrF** & **TER** & **Loss** \\ \hline Transformer (mixed validation set) & 15.7 & 35.8 & 76.5 & 4.13 \\ Fine-tuned (in-domain validation set) & **16.1** & **36.0** & **74.5** & **3.53** \\ \hline \hline \end{tabular}
\end{table} 
Table 8: BLEU, chrF, and TER scores of NMT models fine-tuned on authentic dataset for Wolaytta-English translation. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model (English–Wolaytta)** & **BLEU (\%)** & **chrF** & **TER** & **Loss** \\ \hline Fine-tuned (mixed validation set) & 15.7 & 35.8 & 76.5 & 4.13 \\ Fine-tuned (in-domain validation set) & **16.1** & **36.0** & **74.5** & **3.53** \\ \hline \hline \end{tabular}
\end{table} 
Table 9: BLEU, chrF, and TER scores of NMT models fine-tuned in authentic dataset for English–Wolaytta translation. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model (English–Wolaytta)** & **BLEU (\%)** & **chrF** & **TER** & **Loss** \\ \hline \hline
\begin{tabular}{c} Fine-tuned (mixed validation set) \\ Fine-tuned (in-domain validation set) \\ \end{tabular} & 8.7 & 30.4 & 88.6 & 4.82 \\ Fine-tuned (in-domain validation set) & **9.0** & **31.8** & **86.1** & **4.51** \\ \hline \hline \end{tabular}
\end{table} 
Table 9: BLEU, chrF, and TER scores of NMT models fine-tuned in authentic dataset for English–Wolaytta translation. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model (English–Wolaytta)** & **BLEU (\%)** & **chrF** & **TER** & **Loss** \\ \hline \hline
\begin{tabular}{c} LSTM \\ Bi-LSTM \\ **Transformer** \\ \end{tabular} & 2.8 & 20.5 & 95.0 & 7.28 \\
**Transformer** & **6.2** & **26.0** & **93.9** & 6.26 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: BLEU, chrF, and TER scores of baseline models in English–Wolaytta translation. chrF scores mean that a the translation is closer to the reference (test set), while a higher TER score indicates that a the translation is farther from the reference (test set). ### Baseline

Tables 4 and 5 show the results of our baseline experiments for Wolaytta-English and English-Wolaytta translations, respectively. For Wolaytta-English translation, the Uni-LSTM and Bi-LSTM models demonstrated 6.0 and 6.7 BLEU; 23.9 and 24.7 chrF; and 87.4 and 86.6 TER scores, respectively.