Joint Training for Neural Machine Translation

Description

This book presents four approaches to jointly training bidirectional neural machine translation (NMT) models. First, in order to improve the accuracy of the attention mechanism, it proposes an agreement-based joint training approach to help the two complementary models agree on word alignment matrices for the same training data. Second, it presents a semi-supervised approach that uses an autoencoder to reconstruct monolingual corpora, so as to incorporate these corpora into neural machine translation. It then introduces a joint training algorithm for pivot-based neural machine translation, which can be used to mitigate the data scarcity problem. Lastly it describes an end-to-end bidirectional NMT model to connect the source-to-target and target-to-source translation models, allowing the interaction of parameters between these two directional models. Front Matter ....Pages i-xiii Neural Machine Translation (Yong Cheng)....Pages 1-10 Agreement-Based Joint Training for Bidirectional Attention-Based Neural Machine Translation (Yong Cheng)....Pages 11-23 Semi-supervised Learning for Neural Machine Translation (Yong Cheng)....Pages 25-40 Joint Training for Pivot-Based Neural Machine Translation (Yong Cheng)....Pages 41-54 Joint Modeling for Bidirectional Neural Machine Translation with Contrastive Learning (Yong Cheng)....Pages 55-68 Related Work (Yong Cheng)....Pages 69-74 Conclusion (Yong Cheng)....Pages 75-78