Advancing Transfer Learning Paradigms for Myanmar (Burmese) to Wa (Austroasiatic Language Family) Language Translation

CondenseKnowledge transfer stands as a model for its numerous advantages known for its numerous advantages, such as reducing training time, improving neural network performance in many cases, and minimizing data requirements. In order to tackle this concern, previous research has demonstrated that utilizing pivot-based machine translation systems has the potential to improve translation quality significantly, particularly when the intermediary language closely corresponds to the source and target language pair. This investigation presents an effective method that utilizes pivot transfer learning techniques for the translation task, specifically focusing on converting text from Myanmar to Wa. The intermediary pivot in this process includes translation bridges between Myanmar and English, as well as English and Wa. The investigation empirical examination of 5 key foundational approaches, with a focus on traditional translation model, Transformer NMT, and immediate knowledge transfer as baseline models. The results obtained from this extensive study provide robust evidence in favor of the efficacy of the suggested pivot transfer learning-oriented automatic translation (MT) framework. Clearly, this strategy leads to notably elevated BLEU scores within the domain of the Myanmar to Wa translation system, underscoring its promising applicability and valuable contributions to the automatic translation domain. Given that the Wa language is an Austroasiatic language from Myanmar and a low-resource language closely related to English, our corpus represents a pioneering effort in this domain.