Standard RoBERTa models excel at context but often lack explicit knowledge of language rules. Introduce how the World Atlas of Language Structures (WALS)
Could we train RoBERTa to output zip-compatible representations of WALS features? That would be a form of neural compression, a variational autoencoder for typology. The phrase "136zip best" might then refer to the optimal compression rate—the point where information loss is minimized while model size is reduced.
Standard RoBERTa models excel at context but often lack explicit knowledge of language rules. Introduce how the World Atlas of Language Structures (WALS)
Could we train RoBERTa to output zip-compatible representations of WALS features? That would be a form of neural compression, a variational autoencoder for typology. The phrase "136zip best" might then refer to the optimal compression rate—the point where information loss is minimized while model size is reduced. wals roberta sets 136zip best