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.
@article{arxiv.1906.03129,
title = {Word-based Domain Adaptation for Neural Machine Translation},
author = {Shen Yan and Leonard Dahlmann and Pavel Petrushkov and Sanjika Hewavitharana and Shahram Khadivi},
journal= {arXiv preprint arXiv:1906.03129},
year = {2019}
}
Comments
Published on the proceedings of the International Workshop on Spoken Language Translation (IWSLT), 2018