To further extend the adapter-based approach to leverage typological relatedness between ancient/historical languages and their modern counterparts, several strategies can be implemented. Firstly, incorporating language-specific adapters that capture typological features unique to each language can enhance the model's ability to adapt to linguistic differences. By fine-tuning these adapters on typologically related languages, the model can learn to generalize across language families or language groups. Additionally, creating a shared adapter space where adapters from related languages can interact and share information can facilitate cross-lingual transfer of linguistic features. This shared space can serve as a bridge for transferring knowledge between languages with similar typological characteristics. Moreover, incorporating transfer learning techniques that focus on typological similarities, such as multi-task learning on typologically related tasks, can further enhance the model's ability to adapt to diverse linguistic structures.