Incident-Driven Machine Translation and Name Tagging for Low-resource Languages

We describe novel approaches to tackling the problem of natural language processing for low-resource languages. The approaches are embodied in systems for name tagging and machine translation (MT) that we constructed to participate in the NIST LoReHLT evaluation in 2016. Our methods include universal tools, rapid resource and knowledge acquisition, rapid language projection, and joint methods for MT and name tagging.