Agreement-Based Joint Training for Bidirectional Attention-Based Neural Machine Translation
Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. Pages 2761-2766.
Abstract
The attentional mechanism has proven to be effective in improving end-to-end neural machine translation. However, due to the intricate structural divergence between natural languages, unidirectional attention-based models might only capture partial aspects of attentional regularities. We propose agreement-based joint training for bidirectional attention-based end-to-end neural machine translation. Instead of training source-to-target and target-to-source translation models independently, our approach encourages the two complementary models to agree on word alignment matrices on the same training data. Experiments on Chinese-English and English-French translation tasks show that agreement-based joint training significantly improves both alignment and translation quality over independent training.
@inproceedings{ijcai2016p392,
title = {Agreement-Based Joint Training for Bidirectional Attention-Based Neural Machine Translation},
author = {Cheng, Yong and Shen, Shiqi and He, Zhongjun and He, Wei and Wu, Hua and Sun, Maosong and Liu, Yang},
booktitle = {Proceedings of the Twenty-Fifth International Joint Conference on
Artificial Intelligence, {IJCAI-16}},
publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
editor = {Subbarao Kambhampati},
pages = {2761--2766},
year = {2016},
month = {7},
url = {https://ijcai.org/proceedings/2016/392},
}
TY - CPAPER TI - Agreement-Based Joint Training for Bidirectional Attention-Based Neural Machine Translation AU - Cheng, Yong AU - Shen, Shiqi AU - He, Zhongjun AU - He, Wei AU - Wu, Hua AU - Sun, Maosong AU - Liu, Yang T2 - Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-16 PB - AAAI Press / International Joint Conferences on Artificial Intelligence PY - 2016 SP - 2761 EP - 2766 UR - https://ijcai.org/proceedings/2016/392 ER -Copied
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