@inproceedings{junczys-dowmunt2016the,
author = {Junczys-Dowmunt, Marcin and Dwojak, Tomasz and Sennrich, Rico},
title = {The AMU-UEDIN Submission to the WMT16 News Translation Task: Attention-based NMT Models as Feature Functions in Phrase-based SMT},
booktitle = {Proceedings of the First Conference on Machine Translation (WMT 16)},
year = {2016},
month = {August},
abstract = {This paper describes the AMU-UEDIN submissions to the WMT 2016 shared task on news translation. We explore methods of decode-time integration of attention-based neural translation models with phrase-based statistical machine translation. Efficient batch-algorithms for GPU-querying are proposed and implemented. For English-Russian, our system stays behind the state-of-the-art pure neural models in terms of BLEU. Among restricted systems, manual evaluation places it in the first cluster tied with the pure neural model. For the Russian-English task, our submission achieves the top BLEU result, outperforming the best pure neural system by 1.1 BLEU points and our own phrase-based baseline by 1.6 BLEU. After manual evaluation, this system is the best restricted system in its own cluster. In follow-up experiments we improve results by additional 0.8 BLEU.},
url = {http://approjects.co.za/?big=en-us/research/publication/amu-uedin-submission-wmt16-news-translation-task-attention-based-nmt-models-feature-functions-phrase-based-smt/},
edition = {Proceedings of the First Conference on Machine Translation (WMT 16)},
}