Low-Resource Translation Prototyping

Challenge 

Most machine translation systems neglect smaller or politically marginalized languages. For many communities, there simply isn’t enough data to build usable translation tools using standard models. In this case, we needed Tajiki translations.

Solution

We applied a linguistically informed bootstrapping approach, using related language pairs (in this case Iranian Persian and Tajiki Persian), transfer learning, and domain-specific data alignment to rapidly prototype a functional translation system tailored to the organization’s communication needs.

Outcome

Organization gained practical translation tools that enabled more effective community outreach for previously underserved populations.

Why It Matters

In low-resource language contexts, translation access can mean the difference between inclusion and isolation. Custom-built MT solutions offer a sustainable path for humanitarian, educational, and cultural initiatives where big tech tools fall short.