Joint Training for Pivot-Based Neural Machine Translation
Abstract
While recent neural machine translation approaches have delivered state-of-the-art performance for resource-rich language pairs, they suffer from the data scarcity problem for resource-scarce language pairs. Although this problem can be alleviated by exploiting a pivot language to bridge the source and target languages, the source-to-pivot and pivot-to-target translation models are usually independently trained. In this work, we introduce a joint training algorithm for pivot-based neural machine translation. We propose three methods to connect the two models and enable them to interact with each other during training. Experiments on Europarl andWMTcorpora show that joint training of source-to-pivot and pivot-to-target models leads to significant improvements over independent training across various languages.
Cite
Text
Cheng et al. "Joint Training for Pivot-Based Neural Machine Translation." International Joint Conference on Artificial Intelligence, 2017. doi:10.24963/IJCAI.2017/555Markdown
[Cheng et al. "Joint Training for Pivot-Based Neural Machine Translation." International Joint Conference on Artificial Intelligence, 2017.](https://mlanthology.org/ijcai/2017/cheng2017ijcai-joint/) doi:10.24963/IJCAI.2017/555BibTeX
@inproceedings{cheng2017ijcai-joint,
title = {{Joint Training for Pivot-Based Neural Machine Translation}},
author = {Cheng, Yong and Yang, Qian and Liu, Yang and Sun, Maosong and Xu, Wei},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2017},
pages = {3974-3980},
doi = {10.24963/IJCAI.2017/555},
url = {https://mlanthology.org/ijcai/2017/cheng2017ijcai-joint/}
}