MACHINE TRANSLATION FROM ANCIENT GREEK TO ENGLISH: EXPERIMENTS WITH OPENNMT

Abstract

The current thesis focuses on the application of neural machine translation (NMT) models translating from Ancient Greek to English. The rich morphology, syntax, and vocabulary of the Ancient Greek combined with its status as a low-resource language pair, lead to considerable challenges for translation. Specifically, this study seeks to address the following question: How can NMT models capture the richness and complexity of the source language? Pre-processing of a parallel corpus from Perseus Digital Library and Opus[32] \Tatoeba [33] is done followed by division into training, validation, and testing sets. Multiple NMT models were built using the OpenNMT [19] framework, primarily based on recurrent neural network (RNN) architectures. The top-performing model was an RNN-based model with one one-layer encoderdecoder and a “general” attention mechanism. Despite the modest scores in metrics, including a BLEU score of 8 and METEOR of 0.35, the model exposes limitations in capturing morphosyntactic, semantic, and pragmatic details, especially in longer sentences.