Subword Regularization

Subword regularization is a technique in natural language processing that enhances the robustness and performance of language models by mitigating the limitations of fixed tokenization. Current research focuses on improving the efficiency of subword regularization methods, analyzing the distributional properties of different tokenization schemes (like BPE and MaxMatch), and addressing biases introduced by tokenization errors. This work is significant because it leads to more accurate and resilient language models, impacting various applications such as machine translation, document classification, and named entity recognition, particularly in low-resource settings.