  Residents of developing countries are disproportionately susceptible to
displacement as a result of humanitarian crises. During such crises, language
barriers impede aid workers in providing services to those displaced. To build
resilience, such services must be flexible and robust to a host of possible
languages. \textit{Tarjimly} aims to overcome the barriers by providing a
platform capable of matching bilingual volunteers to displaced persons or aid
workers in need of translating. However, Tarjimly's large pool of translators
comes with the challenge of selecting the right translator per request. In this
paper, we describe a machine learning system that matches translator requests
to volunteers at scale. We demonstrate that a simple logistic regression,
operating on easily computable features, can accurately predict and rank
translator response. In deployment, this lightweight system matches 82\% of
requests with a median response time of 59 seconds, allowing aid workers to
accelerate their services supporting displaced persons.
