# Low-Resource Neural Machine Translation Improvement Using Source-Side Monolingual Data
## 5 Methodology
### Experimental Architecture
#### 5.4.2 Self-Learning
We showed that combining synthetic and authentic parallel datasets of a low-resource language in a self-learning method led to improvements in the BLEU scores of +2.5-2.7 and +2.2-2.4 for Wolaryta-English and English-Wolaryta translations, respectively, over the best-performing baseline. In addition we showed that fine-tuning the NMT models trained in a self-learning approach on an authentic parallel Wolaryta-English dataset improved the BLEU score over the self-learning approach by +0.8-1.2 and +0.3-0.6 for Wolaryta-English and English-Wolaryta translations, respectively. In the future, we would like to study the effect of increasing the size of the source-side monolingual data in a low-resource NMT system. We will also investigate the benefits of using source-side monolingual data for languages similar to Wolaryta that do not have a monolingual dataset, and we will investigate if the proposed approach works for other low-resource languages. Conceptualization, A.L.T. and O.K. ; Methodology, A.L.T., O.K., A.G. and G.S. ; Software, G.S. ; Validation, A.G.; Formal analysis, A.L.T. and O.K. ; Investigation, A.L.T., O.K., A.G. and G.S. ; Resources, A.L.T. and O.K. ; Data curation, A.L.T. ; Writing--original draft, A.L.T. ; Writing--review & editing, O.K. and G.S. ; Visualization, A.G.; Supervision, O.K. and A.G.; Project administration, A.L.T., O.K. and G.S. ; Funding acquisition, O.K., A.G. and G.S. All authors have read and agreed to the published version of the manuscript. The work was performed with partial support from the Mexican Government through the grant A1S-47854 of CONACYT, Mexico and grants 20220852, 20220859, and 20221627 of the Secretaria de Investigacion y Posgrado of the Instituto Politecnico Nacional, Mexico. The authors thank the CONACYT for the computing resources brought to them through the Plataforma de Aprendizaje Profundo para Tecnologias del Lenguaje of the Laboratorio de Supercomputo of the INAOE, Mexico and acknowledge the support of Microsoft through the Microsoft Latin America PhD Award. Not applicable. The dataset and the script used in this paper can be accessed by [[https://github.com/atnafuatx/EthioNMT-datasets](https://github.com/atnafuatx/EthioNMT-datasets)]([https://github.com/atnafuatx/EthioNMT-datasets](https://github.com/atnafuatx/EthioNMT-datasets)). The authors declare no conflict of interest. ## References

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