# Low-Resource Neural Machine Translation Improvement Using Source-Side Monolingual Data
## 5 Methodology
### Experimental Architecture
#### 5.4.2 Self-Learning
Based on these results, we can confirm that the encoder-decoder Bi-LSTM model's performance is better than that of the encoder-decoder Uni-LSTM model, which is explained by the additional layer aggregation in Bi-LSTM: this enabled the model to perform better than the Uni-LSTM. Similarly, for English-Wolaytta translation, the Uni-LSTM and Bi-LSTM models showed 2.1 and 2.8 BLEU; 18.2 and 20.5 chrF; and 97.0 and 95.0 TER scores, respectively. The numbers make it evident that the Bi-LSTM model's performance is better than that of Uni-LSTM; the former took advantage of the additional layer to predict a better reference than Uni-LSTM. Comparing all the results in both encoder-decoder models, we see that the translation into English as the target language was of better quality than the translation from English as the source language; this is due to the morphological complexity of the Wolaytta language and the technological favor of the model for a high-resource language. Comparing the transformer model with both encoder-decoder models, the Transformer outperformed both models in both translations. For Wolaytta-English translation, the transformer model showed a 12.2 BLEU score, which is two times greater than that of the Bi-LSTM model; this shows that the input sequence processing technique and the attention mechanism of the Transformer boosted the performance of the model compared to the Uni-LSTM and Bi-LSTM models. In the latter models, we did not use an attention mechanism, and the way they process the input sequence is different from that of the Transformer. The Uni-LSTM and Bi-LSTM models process sequences of symbol representations step by step and separately, while the Transformer processes all the input sequences in one step. Similarly for English-Wolaytta translation, the Transformer model showed a 6.2 BLEU score, which is three times higher than that of Bi-LSTM. Observing the performance of the transformer in both translations, we see that it is challenged when English is the source language compared to when English is the target language. Thus, from the three baseline models, the Transformer model outperformed the other models; therefore, we selected the Transformer model for the rest of the experiments. As we have already discussed, the goal of the baseline experiment was to choose the model that works best for our experiments. ### Self-Learning

Tables 6 and 7 present the results of the self-learning experiments for Wolaytta-English and English-Wolaytta translations, respectively. We conducted two experiments to investigate the impact of using source-side monolingual data to improve NMT for a low-resource language, as discussed in Section 5.4.2. In the experiments, we combined synthetic and authentic parallel datasets using two methods. In the first experiment, we used a mixed validation set obtained by combining synthetic and authentic parallel datasets further splitting the resulting dataset into training and validation sets in the ratio of 80:20, respectively, and then we applied a test set from the authentic parallel dataset. In the second experiment, after training the model with both synthetic and authentic parallel datasets, we used validation and test sets from the same domain. As we can see from the results, training the selected transformer model on a combination of synthetic and authentic parallel datasets showed an increment in the BLEU, chrF, and TER scores of the NMT model in both translation directions. For Wolaytta-English translation, applying the self-learning approach in the mixed validation set and the in-domain validation set showed 14.7 and 14.9 BLEU; 34.7 and 35.1 chrF; and 78.1 and 77.6 TER scores, respectively. As it can be seen from the results, the model trained on a combination of synthetic and authentic parallel datasets and validated on an in-domain set outperformed the model trained and validated on a mixed dataset by +0.2 BLEU, +0.4 chrF, and \(-\)0.5 TER scores.