The AMU-UEDIN Submission to the WMT16 News Translation Task: Attention-based NMT Models as Feature Functions in Phrase-based SMT

Understand

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.