We have replaced the dependency on large semantic embedding models for the meaning scorer with a more lightweight, deterministic approach using WordNet's Wu-Palmer similarity metric. This refactor improves maintainability by removing the need for heavy external ML libraries like sentence-transformers, while maintaining a robust way to evaluate semantic consistency between translated tokens.