Abstract
Machine translation (MT) for low-resource languages such as Ge'ez, an ancient language that is no longer spoken in daily life, 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.
Abstract (translated)
机器翻译（MT）对于像Ge'ez这样的低资源语言面临诸如词汇缺失、领域不匹配和缺乏充分标注训练数据等挑战。在这项工作中，我们探讨了各种方法来提高Ge'ez MT，包括从相关语言进行迁移学习、优化共享词汇和词段划分方法、对大型预训练模型进行微调以及使用大型语言模型（LLMs）进行微光翻译。我们基于语言相关性开发了一种多语言神经机器翻译（MNMT）模型，与标准双语模型相比，平均性能提高了约4 BLEU。我们还尝试微调NLLB-200模型，这是目前最先进的翻译模型之一，但发现仅用4k个训练样本对Ge'ez进行微光翻译时，其表现不佳。此外，我们还尝试使用GPT-3.5，这是一种最先进的LLM，进行微光翻译，它利用基于嵌入相似性的检索从同义词库中查找上下文示例。我们观察到，GPT-3.5在没有初始知识的情况下，实现了令人惊叹的BLEU得分9.2，但仍然低于MNMT基线15.2。我们的工作揭示了不同方法在低资源和 ancient language MT 中的潜力和限制。
URL
https://arxiv.org/abs/2311.14530
