Abstract
Machine translation systems are very sensitive to the domains they were trained on. Several domain adaptation techniques have already 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.- Anthology ID:
- R17-1049
- Volume:
- Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017
- Month:
- September
- Year:
- 2017
- Address:
- Varna, Bulgaria
- Venue:
- RANLP
- SIG:
- Publisher:
- INCOMA Ltd.
- Note:
- Pages:
- 372–378
- Language:
- URL:
- https://doi.org/10.26615/978-954-452-049-6_049
- DOI:
- 10.26615/978-954-452-049-6_049
- Cite (ACL):
- Catherine Kobus, Josep Crego, and Jean Senellart. 2017. Domain Control for Neural Machine Translation. In Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017, pages 372–378, Varna, Bulgaria. INCOMA Ltd..
- Cite (Informal):
- Domain Control for Neural Machine Translation (Kobus et al., RANLP 2017)
- PDF:
- https://doi.org/10.26615/978-954-452-049-6_049