Replicable Benchmarking of Neural Machine Translation (NMT) on Low-Resource Local Languages in Indonesia

• URL: http://arxiv.org/abs/2311.00998v1
• Date: Thu, 2 Nov 2023 05:27:48 GMT
• Title: Replicable Benchmarking of Neural Machine Translation (NMT) on Low-Resource Local Languages in Indonesia
• Authors: Lucky Susanto, Ryandito Diandaru, Adila Krisnadhi, Ayu Purwarianti, Derry Wijaya
• Abstract summary: This study comprehensively analyzes training NMT systems for four low-resource local languages in Indonesia: Javanese, Sundanese, Minangkabau, and Balinese. Our research demonstrates that despite limited computational resources and textual data, several of our NMT systems achieve competitive performances.
• Score: 4.634142034755327
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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