# Low-Resource Neural Machine Translation Improvement Using Source-Side Monolingual Data
## 5 Methodology
### Experimental Architecture
#### 5.4.2 Self-Learning
This shows that using an in-domain validation set helped the NMT model fine-tune hyperparameters during the training phase, which led to better performance than using a mixed validation set. We observed that mixing synthetic and authentic parallel datasets improved NMT quality by the +2.5-2.7 BLEU, +2.9-3.3 chrF, and \(-\)2.6-2.1 TER scores for the Wolsytta-English translation compared with the baseline transformer model. In the same way, applying the self-learning method to the mixed validation set and the in-domain validation set produced 8.4 and 8.6 BLEU; 30.3 and 30.6 chrF; and 89.7 and 88.9 TER scores for English-Wolsytta translation, respectively. For the English-Wolsytta translation, the model trained on the in-domain validation set outperformed the model trained on the mixed validation set. When the overall performance of the English-Wolsytta translation was compared to the baseline transformer model, it was found that mixing synthetic and authentic parallel datasets improved NMT quality by +2.2-2.4 BLEU, +4.3-4.6 chrF, and \(-\)4.2-5 TER scores. Therefore, based on the above results, we can say that utilizing only source-side monolingual data improves the performance of NMT for Wolsytta as a low-resource language in both translation directions if we combine synthetic and authentic parallel datasets for training and use authentic datasets for validation and testing. ### Fine-Tuning

We fine-tuned each selected model for up to 10k steps using early stopping on the validation set, and we set the validation score threshold to stop training at BLEU score of 0.2 over the last four iterations. By using the authentic parallel datasets, we fine-tuned the two models described in Section 7.2 to further improve the NMT performance of Wolsytta-English translation in both directions. The results of the fine-tuning self-trained NMT models for Wolsytta-English translation using mixed validation and in-domain validation sets show 15.7 and 16.1 BLEU; 35.8 and 36.0 chrF; and 76.5 and 74.5 TER scores, respectively. Fine-tuning the NMT models trained on authentic parallel datasets increased the NMT quality by +0.8-1.2 BLEU, +0.7-0.9 chrF, and \(-\)1.1-3.1 TER scores compared to the NMT models trained on a mix of authentic and synthetic parallel datasets. In the same way, fine-tuning of the self-trained NMT models for English-Wolsytta translation using mixed validation and in-domain validation sets led to 8.7 and 9.0 BLEU; 30.4 and 31.8 chrF; and 88.6 and 86.1 TER scores, respectively. This showed the improvement of NMT quality by +0.3-0.6 BLEU, +0.2-1.2 chrF, and \(-\)0.9-2.8 TER scores over the top-performing NMT model trained on the mixed dataset. Based on the above results, we can say that using the authentic parallel dataset to fine-tune NMT models trained on both synthetic and authentic data improves the NMT performance in both directions for low-resource Wolsytta-English. The results of the test sets show that fine-tuning the NMT models that were trained on in-domain validation sets improves performance in both directions of translation. ### Limitation of the Study

In this paper, we did not evaluate the confidence score, which is widely used in the evaluation of translation quality. Further study of the translation qualities of monolingual data sets before combining them with authentic datasets during the training phase would improve the NMT performance only by sampling the dataset based on the confidence score. In addition, the NMT system for low-resource languages would be better if it was possible to use a monolingual dataset of a low-resource language in a multilingual setting. ## 8 Conclusions

In this paper, we studied whether a source-side monolingual dataset of a low-resource language could be used to improve the NMT system for such language. As a low-resource language translation example, we used the Wolsytta-English language pair. We used an approach called self-learning and fine-tuning, along with synthetic and authentic parallel datasets for the Wolaryta-English language pair in both directions of translation.