Training Deeper Neural Machine Translation Models with Transparent Attention

While current state-of-the-art NMT models, such as RNN seq2seq and Transformers, possess a large number of parameters, they are still shallow in comparison to convolutional models used for both text and vision applications. In this work we attempt to train significantly (2-3x) deeper Transformer and...

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Bibliographische Detailangaben

Hauptverfasser:	Bapna, Ankur, Chen, Mia Xu, Firat, Orhan, Cao, Yuan, Wu, Yonghui
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer Science - Artificial Intelligence Computer Science - Computation and Language Computer Science - Learning
Online-Zugang:	Volltext bestellen
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