Cultural Data Collection and Analysis Support
Organizational Context
United Nations Peacekeeping missions are vital tools for global peace and security, helping nations transition from conflict to stability. They protect civilians, promote non-violent conflict resolution, and build foundations for lasting peace. The Real-Time Translation (RTT) Project, led by the Department of Peace Operations, aims to enhance communication between peacekeepers and local communities by developing translation tools for underserved languages and dialects, especially in connectivity-limited environments.
Job Purpose
This role supports the Real-Time Translation (RTT) Project by developing and integrating structured cultural datasets into translation tools. The project's objective is to strengthen communication between UN peacekeeping personnel and the communities they serve, particularly in regions with limited translation resources. By reducing language barriers, the project enhances the capacity of peacekeepers to understand community concerns and facilitate information exchange. The assignment will contribute to improving the tools' sensitivity to local context, ensuring more effective and culturally appropriate communication during field engagements and patrols.
Responsibilities
The primary responsibility is to develop a structured cultural dataset for integration into the Real-Time Translation (RTT) tool under the guidance of the Social Innovation and Special Projects Team (SISPT). This involves processing selected cultural materials into a consistent data format, assessing and refining data quality through linguistic and cultural review, and supporting the integration of the dataset into the RTT tool. The volunteer will also evaluate the tool's performance with culturally specific content. The final deliverable includes a documented cultural dataset with methodology, integration procedures, and evaluation findings.
Work Experience
Experience is required in developing cultural datasets and evaluation benchmarks for language models, particularly for underrepresented languages and cultures. Additionally, experience in data processing and annotation, multilingual language model development and evaluation, and technical documentation is necessary.
Skills
Key skills include expertise in data science, information management, and Artificial Intelligence. Proficiency in English (fluent) is required. The role also necessitates experience with data processing, annotation, and multilingual language model development and evaluation.