Low Resource Neural Machine Translation

Low-resource neural machine translation (NMT) focuses on building accurate translation systems for languages with limited parallel training data. Current research emphasizes data augmentation techniques, such as generative adversarial networks (GANs) and back-translation, alongside leveraging multilingual models and knowledge distillation to transfer knowledge from high-resource languages. These efforts aim to improve translation quality for under-resourced languages, impacting both scientific understanding of cross-lingual transfer and practical applications like cross-cultural communication and information access. Furthermore, research explores alternative training objectives and architectures, including those based on active learning and morphological modeling, to enhance model performance and generalization in data-scarce scenarios.