BBN’s low-resource machine translation for the LoReHLT 2016 evaluation

We describe BBN’s contribution to the machine translation (MT) task in the LoReHLT 2016 evaluation, focusing on the techniques and methodologies employed to build the Uyghur–English MT systems in low-resource conditions. In particular, we discuss the data selection process, morphological segmentation of the source, neural network feature models, and our use of a native informant and related language resources. Our final submission for the evaluation was ranked first among all participants.