印度尼西亚低资源本地语言神经机器翻译（NMT）的可复现基准评测
Replicable Benchmarking of Neural Machine Translation (NMT) on Low-Resource Local Languages in Indonesia
AI总结：
本文针对印尼四种低资源语言构建可复现NMT基准，系统比较不同训练方法与数据规模，并初步探索大模型合成平行数据，证明有限资源下NMT性能可媲美零样本gpt-3.5-turbo。
摘要：
针对印度尼西亚低资源本地语言的神经机器翻译（NMT）面临重大挑战，包括需要具有代表性的基准以及数据可用性有限。本工作通过全面分析四种印度尼西亚低资源本地语言——爪哇语、巽他语、米南加保语和巴厘语——的NMT系统训练来应对这些挑战。我们的研究涵盖多种训练方法、范式、数据规模，并对使用大型语言模型生成低资源语言合成平行数据进行了初步研究。我们揭示了低资源语言翻译实用策略的具体趋势和见解。我们的研究表明，尽管计算资源和文本数据有限，我们的一些NMT系统仍取得了具有竞争力的性能，可与零样本gpt-3.5-turbo的翻译质量相媲美。这些发现显著推动了低资源语言的NMT发展，为类似情境下的研究人员提供了有价值的指导。
英文摘要：
Neural machine translation (NMT) for low-resource local languages in Indonesia faces significant challenges, including the need for a representative benchmark and limited data availability. This work addresses these challenges by comprehensively analyzing training NMT systems for four low-resource local languages in Indonesia: Javanese, Sundanese, Minangkabau, and Balinese. Our study encompasses various training approaches, paradigms, data sizes, and a preliminary study into using large language models for synthetic low-resource languages parallel data generation. We reveal specific trends and insights into practical strategies for low-resource language translation. Our research demonstrates that despite limited computational resources and textual data, several of our NMT systems achieve competitive performances, rivaling the translation quality of zero-shot gpt-3.5-turbo. These findings significantly advance NMT for low-resource languages, offering valuable guidance for researchers in similar contexts.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
