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Towards Effective Machine Translation For Karachay-Balkar
Neural machine translation (NMT) is often heralded as the most effective approach to machine translation due to its success on language pairs with enormous parallel corpora. However, neural methods produce less than ideal results on low-resource languages their performance is evaluated using metrics like the Bilingual Evaluation Understudy (BLEU) score. One alternative is rule-based machine-translation (RBMT), but it too has drawbacks. I am currently writing an undergraduate thesis comparing the two approaches onholistically based on efficacy, ethicality, and utility to low-resource language communities. Using the language Karachay-Balkar as a case-study, my thesis investigates how two free and open-source machine translation packages, Apertium (rule-based) and JoeyNMT (neural), might support community-driven machine translation development.