Is Neural Machine Translation Ready for Deployment? A Case Study on 30 Translation Directions

Understand

In this paper we provide the largest published comparison of translation quality for phrase-based SMT and neural machine translation across 30 translation directions.

• For ten directions we also include hierarchical phrase-based MT.
• Experiments are performed for the recently published United Nations Parallel Corpus v1.0 and its large six-way sentence-aligned subcorpus.
• In the second part of the paper we investigate aspects of translation speed, introducing AmuNMT, our efficient neural machine translation decoder.