Neural Machine Translation Training in a Multi-Domain Scenario
Hassan Sajjad, Nadir Durrani, Fahim Dalvi, Yonatan Belinkov, Stephan Vogel
Abstract
In this paper, we explore alternative ways to train a neural machine translation system in a multi-domain scenario. We investigate data concatenation (with fine tuning), model stacking (multi-level fine tuning), data selection and multi-model ensemble. Our findings show that the best translation quality can be achieved by building an initial system on a concatenation of available out-of-domain data and then fine-tuning it on in-domain data. Model stacking works best when training begins with the furthest out-of-domain data and the model is incrementally fine-tuned with the next furthest domain and so on. Data selection did not give the best results, but can be considered as a decent compromise between training time and translation quality. A weighted ensemble of different individual models performed better than data selection. It is beneficial in a scenario when there is no time for fine-tuning an already trained model.- Anthology ID:
- 2017.iwslt-1.10
- Volume:
- Proceedings of the 14th International Conference on Spoken Language Translation
- Month:
- December 14-15
- Year:
- 2017
- Address:
- Tokyo, Japan
- Venue:
- IWSLT
- SIG:
- SIGSLT
- Publisher:
- International Workshop on Spoken Language Translation
- Note:
- Pages:
- 66–73
- Language:
- URL:
- https://aclanthology.org/2017.iwslt-1.10
- DOI:
- Cite (ACL):
- Hassan Sajjad, Nadir Durrani, Fahim Dalvi, Yonatan Belinkov, and Stephan Vogel. 2017. Neural Machine Translation Training in a Multi-Domain Scenario. In Proceedings of the 14th International Conference on Spoken Language Translation, pages 66–73, Tokyo, Japan. International Workshop on Spoken Language Translation.
- Cite (Informal):
- Neural Machine Translation Training in a Multi-Domain Scenario (Sajjad et al., IWSLT 2017)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/2017.iwslt-1.10.pdf