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Training Deeper Neural Machine Translation Models with Transparent Attention

2018-08-22EMNLP 2018Code Available0· sign in to hype

Ankur Bapna, Mia Xu Chen, Orhan Firat, Yuan Cao, Yonghui Wu

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Abstract

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 Bi-RNN encoders for machine translation. We propose a simple modification to the attention mechanism that eases the optimization of deeper models, and results in consistent gains of 0.7-1.1 BLEU on the benchmark WMT'14 English-German and WMT'15 Czech-English tasks for both architectures.

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Machine TranslationNMTTranslation

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