Word-based Domain Adaptation for Neural Machine Translation
Shen Yan, Leonard Dahlmann, Pavel Petrushkov, Sanjika Hewavitharana, Shahram Khadivi
Abstract
In this paper, we empirically investigate applying word-level weights to adapt neural machine translation to e-commerce domains, where small e-commerce datasets and large out-of-domain datasets are available. In order to mine in-domain like words in the out-of-domain datasets, we compute word weights by using a domain-specific and a non-domain-specific language model followed by smoothing and binary quantization. The baseline model is trained on mixed in-domain and out-of-domain datasets. Experimental results on En → Zh e-commerce domain translation show that compared to continuing training without word weights, it improves MT quality by up to 3.11% BLEU absolute and 1.59% TER. We have also trained models using fine-tuning on the in-domain data. Pre-training a model with word weights improves fine-tuning up to 1.24% BLEU absolute and 1.64% TER, respectively.- Anthology ID:
- 2018.iwslt-1.5
- Volume:
- Proceedings of the 15th International Conference on Spoken Language Translation
- Month:
- October 29-30
- Year:
- 2018
- Address:
- Brussels
- Editors:
- Marco Turchi, Jan Niehues, Marcello Frederico
- Venue:
- IWSLT
- SIG:
- SIGSLT
- Publisher:
- International Conference on Spoken Language Translation
- Note:
- Pages:
- 31–38
- Language:
- URL:
- https://aclanthology.org/2018.iwslt-1.5
- DOI:
- Cite (ACL):
- Shen Yan, Leonard Dahlmann, Pavel Petrushkov, Sanjika Hewavitharana, and Shahram Khadivi. 2018. Word-based Domain Adaptation for Neural Machine Translation. In Proceedings of the 15th International Conference on Spoken Language Translation, pages 31–38, Brussels. International Conference on Spoken Language Translation.
- Cite (Informal):
- Word-based Domain Adaptation for Neural Machine Translation (Yan et al., IWSLT 2018)
- PDF:
- https://preview.aclanthology.org/naacl-24-ws-corrections/2018.iwslt-1.5.pdf