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

@article{Hermjakob2017IncidentDrivenMT,
  title={Incident-Driven Machine Translation and Name Tagging for Low-resource Languages},
  author={Ulf Hermjakob and Qiang Li and Daniel Marcu and Jonathan May and Sebastian J. Mielke and Nima Pourdamghani and Michael Pust and Xing Shi and Kevin Knight and Tomer Levinboim and Kenton Murray and David Chiang and Boliang Zhang and Xiaoman Pan and Di Lu and Ying Lin and Heng Ji},
  journal={Machine Translation},
  year={2017},
  volume={32},
  pages={59-89}
}
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. 
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