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

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Machine Translation


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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We would like to thank other ELISA team members who contributed to resource construction and system preparation before the evaluation: Chris Callison-Burch (UPenn), Aliya Deri (USC) and Ashish Vaswani (Google). We thank Billy Wagner from Next Century for running the LTDE to produce name tagging runs. This work was supported by the U.S. Defense Advanced Research Projects Agency (DARPA) LORELEI Program No. HR0011-15-C-0115 and ARL/ARO MURI W911NF-10-1-0533. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on.

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Correspondence to Kevin Knight.

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Hermjakob, U., Li, Q., Marcu, D. et al. Incident-Driven Machine Translation and Name Tagging for Low-resource Languages. Machine Translation 32, 59–89 (2018).

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