Joint Training for Neural Machine Translation (Hardcover, 1st ed. 2019)

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.

General

Imprint:	Springer Verlag, Singapore
Country of origin:	Singapore
Series:	Springer Theses
Release date:	September 2019
First published:	2019
Authors:	Yong Cheng
Dimensions:	235 x 155mm (L x W)
Format:	Hardcover
Pages:	78
Edition:	1st ed. 2019
ISBN-13:	978-981-3297-47-0
Categories:	Books > Computing & IT > Applications of computing > Artificial intelligence > Natural language & machine translation Promotions
LSN:	981-3297-47-6
Barcode:	9789813297470

Is the information for this product incomplete, wrong or inappropriate? Let us know about it.

Does this product have an incorrect or missing image? Send us a new image.

Is this product missing categories? Add more categories.

Review This Product

No reviews yet - be the first to create one!