Modernizing Philology Education with Neural Machine Translation of Ancient Languages

The introduction of neural machine translation (NMT) into the teaching of philology is an opportunity that can radically change the process of decoding and teaching ancient texts in a more accurate and accessible way. This paper discusses a new strategy that will use high-level methods of translation to facilitate language knowledge and the conservation of heritage. The current approaches are mostly inadequate to retain the semantic richness and contextual depth of ancient languages, mainly because of low linguistic data and obsolete systems of translations. The consequences of these concerns are disjointed interpretations and hinder learning. To overcome these issues, it creates a proposal of a Transformer-Based Neural Machine Translation (T-NMT) architecture that is aimed at translating and maintaining the semantic structure of ancient texts. Through attention mechanisms and contextual embeddings, T-NMT is able to guarantee further linguistic and meaning alignment of language pairs. The suggested approach can be used in the curriculum of philology to enable comparative study of languages, digital preservation, and to make students interested in historically significant content. It is also used to aid endangered or extinct languages through automation of translation and reduces experts reliance. We have shown that T-NMT drastically increases the level of translation coherence, preserves semantic status, and increases the pedagogic quality of ancient textual information. This practice demonstrates great prospects in the development of AI-based philological education and cultural conservation.

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