Yaregal AsabieAberash Berhe2025-08-172025-08-172024-06https://etd.aau.edu.et/handle/123456789/6866This thesis presents the first attempt at building a transformer based neural machine translation system for the language pair of Geez and Tigrigna. Geez and Tigrigna are closely related Semitic languages, with Geez being the liturgical language of the Eritrean and Ethiopian Orthodox churches, and Tigrigna being a widely spoken language in Eritrea and parts of Ethiopia. Due to the lack of publicly available parallel corpora for this language pair, the thesis describes the manual collection and curation of a new Geez-Tigrigna parallel dataset, which consists of 10,362 sentence pairs. This process is detailed as it proved to be a laborious and time-consuming task given the limited availability of translated text between the two languages. The architecture of the proposed neural machine translation system is based on the transformer model, which has shown state-of-the-art performance on many language pairs. To address the challenges of translating between low-resource languages like Geez and Tigrigna, an alignment-based approach is integrated into the standard transformer architecture. This alignment mechanism aims to better capture the relationships between source and target language elements during the translation process. The word-level alignments between the parallel sentences are done manually. Experiments are conducted to compare the performance of attention-based recurrent neural network model, a standard transformer model, and the proposed alignment-augmented transformer model. The results show that the standard transformer model achieved a BLEU score of 54%, outperforming the RNN model, which had a BLEU score of 46%. Further improvements were made by integrating the alignment mechanism into the transformer architecture, resulting in an alignment-augmented transformer model that achieved a BLEU score of 63%. These findings demonstrate the feasibility of building neural machine translation systems for low-resource language pairs like Geez and Tigrigna, and that the proposed alignment-based modifications to the transformer architecture can lead to significant improvements in translation quality compared to the standard transformer model.en-USMachine TranslationNeural Machine TranslationTransformerEncoder - Decoder ModelAlignmentThesis