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

Abstract

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. We demonstrate that current neural machine translation could already be used for in-production systems when comparing words-per-second ratios.

Accepted for presentation at IWSLT 2016, Seattle

References in corpus (3)

• Neural Machine Translation by Jointly Learning to Align and Translate
• Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
• ADADELTA: An Adaptive Learning Rate Method

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