2020/11/30 by Divyansh Agarwal, Agarwal, Divyansh, Yuta Baba +9
Computer Science · #Computation and Language (cs.CL) #Computers and Society (cs.CY) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Multimodal Machine Learning Applications #Natural Language Processing Techniques #Topic Modeling
paper · pdf · doi:10.48550/arxiv.2012.02595
openalex publication_date 2020/11/30 · openalex created_date 2020/12/21 · openalex updated_date 2026/07/28
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. 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.