Subword Level Model

Subword-level modeling in natural language processing aims to improve the efficiency and accuracy of language models by representing words as sequences of smaller units, balancing the limitations of character-level and word-level approaches. Current research focuses on optimizing subword segmentation algorithms (like Byte Pair Encoding), integrating subword models with other architectures (e.g., state space models, Conformers), and exploring alternative token-free methods operating directly on bytes. These advancements enhance performance in various tasks, including machine translation, speech recognition, and grammatical error correction, particularly for morphologically rich or low-resource languages, and offer improved robustness to noise and unseen data.