Domain Control for Neural Machine Translation
Abstract
Machine translation systems are very sensitive to the domains they were trained on. Several domain adaptation techniques have been deeply studied. We propose a new technique for neural machine translation (NMT) that we call domain control which is performed at runtime using a unique neural network covering multiple domains. The presented approach shows quality improvements when compared to dedicated domains translating on any of the covered domains and even on out-of-domain data. In addition, model parameters do not need to be re-estimated for each domain, making this effective to real use cases. Evaluation is carried out on English-to-French translation for two different testing scenarios. We first consider the case where an end-user performs translations on a known domain. Secondly, we consider the scenario where the domain is not known and predicted at the sentence level before translating. Results show consistent accuracy improvements for both conditions.
- Publication:
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arXiv e-prints
- Pub Date:
- December 2016
- DOI:
- 10.48550/arXiv.1612.06140
- arXiv:
- arXiv:1612.06140
- Bibcode:
- 2016arXiv161206140K
- Keywords:
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- Computer Science - Computation and Language
- E-Print:
- Published in RANLP 2017