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    <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.</abstract>
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%0 Conference Proceedings
%T Word-based Domain Adaptation for Neural Machine Translation
%A Yan, Shen
%A Dahlmann, Leonard
%A Petrushkov, Pavel
%A Hewavitharana, Sanjika
%A Khadivi, Shahram
%Y Turchi, Marco
%Y Niehues, Jan
%Y Frederico, Marcello
%S Proceedings of the 15th International Conference on Spoken Language Translation
%D 2018
%8 oct 29 30
%I International Conference on Spoken Language Translation
%C Brussels
%F yan-etal-2018-word
%X 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.
%U https://aclanthology.org/2018.iwslt-1.5
%P 31-38

Markdown (Informal)

[Word-based Domain Adaptation for Neural Machine Translation](https://aclanthology.org/2018.iwslt-1.5) (Yan et al., IWSLT 2018)

• Word-based Domain Adaptation for Neural Machine Translation (Yan et al., IWSLT 2018)

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.