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arXiv · 1512.04650

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

Yong Cheng·Shiqi Shen·Zhongjun He·Wei He·Hua Wu·Maosong Sun·Yang Liu
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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.

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Yong Cheng, Shiqi Shen, Zhongjun He, Wei He, Hua Wu, Maosong Sun, Yang Liu. 2015-12-15. Agreement-based Joint Training for Bidirectional Attention-based Neural Machine Translation. https://arxiv.org/abs/1512.04650

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