NSL-MT: Linguistically Informed Negative Samples for Efficient Machine Translation in Low-Resource Languages

Negative Space Learning MT (NSL-MT) represents a significant advancement in machine translation techniques, particularly for low-resource languages where annotated parallel corpora are scarce. By encoding linguistic constraints as severity-weighted penalties in the loss function, NSL-MT effectively teaches models what not to generate, leading to substantial performance improvements. The method has demonstrated BLEU score gains of 3-12% for models that are already performing well and an impressive 56-89% for those lacking initial support. Moreover, NSL-MT enhances data efficiency, providing a 5x multiplier that allows training with just 1,000 examples to match or even surpass the performance of traditional training methods that require 5,000 examples. This innovation not only optimizes the training process but also addresses the challenges faced in low-resource language settings, making it a crucial development in the field of artificial intelligence and machine translation.