This paper investigates the development of bidirectional neural machine translation (NMT) systems between German (a high-resource language) and Bavarian (a low-resource language). The authors explore various techniques to address the challenges of low-resource languages, such as data scarcity and noisy data.
The authors first establish a baseline Transformer model using preprocessed parallel data. They then apply back-translation to generate additional silver-paired data, which leads to significant improvements in translation quality. Finally, they experiment with transfer learning by using a German-French parent model to initialize the child model for German-Bavarian translation.
The evaluation uses a combination of BLEU, chrF, and TER metrics to capture different linguistic characteristics. Statistical significance analysis with Bonferroni correction is performed to ensure robust results.
The key findings are:
The authors also provide a qualitative analysis of the translation outputs, highlighting the challenges posed by dialectal variations and the need for a more refined and standardized Bavarian corpus. They conclude by proposing future research directions, including the curation of a high-quality German-Bavarian dataset and the investigation of dialect identification techniques.
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by Wan-Hua Her,... at arxiv.org 04-15-2024
https://arxiv.org/pdf/2404.08259.pdfDeeper Inquiries