  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 English to
Chinese e-commerce domain translation show that compared to continuing training
without word weights, it improves MT quality by up to 2.11% 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.
