Low Resource Language Pair

Low-resource language pairs, encompassing languages with limited parallel text data for machine translation (MT), pose a significant challenge in natural language processing. Current research focuses on improving MT performance for these pairs through techniques like data augmentation, filtering noisy data using cross-lingual sentence embeddings, and refining model architectures such as Transformers and Mixture of Experts (MoE) models, often incorporating self-reflection or few-shot learning methods. These advancements aim to enhance translation accuracy and efficiency, particularly for morphologically complex languages, ultimately bridging communication gaps and fostering multilingual access to information. Addressing security vulnerabilities, such as backdoor attacks, within these systems is also a growing area of concern.