Toward a full-scale neural machine translation in production: the Booking.com use case
arXiv:1709.05820
Abstract
While some remarkable progress has been made in neural machine translation (NMT) research, there have not been many reports on its development and evaluation in practice. This paper tries to fill this gap by presenting some of our findings from building an in-house travel domain NMT system in a large scale E-commerce setting. The three major topics that we cover are optimization and training (including different optimization strategies and corpus sizes), handling real-world content and evaluating results.
arXiv comment11 pages, 4 figures, presented at MT Summit XVI, Commercial MT Users and Translators Track
References in corpus (6)
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- On the Properties of Neural Machine Translation: Encoder-Decoder Approaches
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Cited by in corpus (5)
- OpenNMT: Neural Machine Translation Toolkit
- RETURNN as a Generic Flexible Neural Toolkit with Application to Translation and Speech Recognition
- Multi-Domain Adaptation in Neural Machine Translation Through Multidimensional Tagging
- Assessing Crosslingual Discourse Relations in Machine Translation
- Neural Machine Translation: A Review and Survey