Abstract
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.
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Notes
LDC catalogue number LDC2011T07.
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Acknowledgements
This work was supported by DARPA/I2O under the LORELEI program. The views, opinions, and/or findings contained in this article are those of the author and should not be interpreted as representing the official views or policies, either expressed or implied, of the Defense Advanced Research Projects Agency or the Department of Defense.
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Work done while Hendra Setiawan was at Raytheon BBN Technologies.
This material is based upon work supported by the Defense Advanced Research Projects Agency (DARPA) under Contract No. HR0011-15-C-0113. The views, opinions and/or findings expressed are those of the author and should not be interpreted as representing the official views or policies of the Department of Defense or the U.S. Government. Distribution Statement ‘A’ (Approved for Public Release by DARPA on Aug 29, 2017 (DISTAR Approval #28392), Distribution Unlimited).
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Setiawan, H., Huang, Z. & Zbib, R. BBN’s low-resource machine translation for the LoReHLT 2016 evaluation. Machine Translation 32, 45–57 (2018). https://doi.org/10.1007/s10590-017-9206-2
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DOI: https://doi.org/10.1007/s10590-017-9206-2