  Machine translation (MT) for low-resource languages such as Ge'ez, an ancient
language that is no longer the native language of any community, faces
challenges such as out-of-vocabulary words, domain mismatches, and lack of
sufficient labeled training data. In this work, we explore various methods to
improve Ge'ez MT, including transfer-learning from related languages,
optimizing shared vocabulary and token segmentation approaches, finetuning
large pre-trained models, and using large language models (LLMs) for few-shot
translation with fuzzy matches. We develop a multilingual neural machine
translation (MNMT) model based on languages relatedness, which brings an
average performance improvement of about 4 BLEU compared to standard bilingual
models. We also attempt to finetune the NLLB-200 model, one of the most
advanced translation models available today, but find that it performs poorly
with only 4k training samples for Ge'ez. Furthermore, we experiment with using
GPT-3.5, a state-of-the-art LLM, for few-shot translation with fuzzy matches,
which leverages embedding similarity-based retrieval to find context examples
from a parallel corpus. We observe that GPT-3.5 achieves a remarkable BLEU
score of 9.2 with no initial knowledge of Ge'ez, but still lower than the MNMT
baseline of 15.2. Our work provides insights into the potential and limitations
of different approaches for low-resource and ancient language MT.
