Proceedings

Agreement-Based Joint Training for Bidirectional Attention-Based Neural Machine Translation

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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.

Yong Cheng, Shiqi Shen, Zhongjun He, Wei He, Hua Wu, Maosong Sun, and Yang Liu. 2016. Agreement-Based Joint Training for Bidirectional Attention-Based Neural Machine Translation. In Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-16. IJCAI, 2761–2766. https://ijcai.org/proceedings/2016/392
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