RETURNN as a Generic Flexible Neural Toolkit with Application to Translation and Speech Recognition

Albert Zeyer, Tamer Alkhouli, Hermann Ney


Abstract
We compare the fast training and decoding speed of RETURNN of attention models for translation, due to fast CUDA LSTM kernels, and a fast pure TensorFlow beam search decoder. We show that a layer-wise pretraining scheme for recurrent attention models gives over 1% BLEU improvement absolute and it allows to train deeper recurrent encoder networks. Promising preliminary results on max. expected BLEU training are presented. We are able to train state-of-the-art models for translation and end-to-end models for speech recognition and show results on WMT 2017 and Switchboard. The flexibility of RETURNN allows a fast research feedback loop to experiment with alternative architectures, and its generality allows to use it on a wide range of applications.
Anthology ID:
P18-4022
Volume:
Proceedings of ACL 2018, System Demonstrations
Month:
July
Year:
2018
Address:
Melbourne, Australia
Editors:
Fei Liu, Thamar Solorio
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
128–133
Language:
URL:
https://aclanthology.org/P18-4022
DOI:
10.18653/v1/P18-4022
Bibkey:
Cite (ACL):
Albert Zeyer, Tamer Alkhouli, and Hermann Ney. 2018. RETURNN as a Generic Flexible Neural Toolkit with Application to Translation and Speech Recognition. In Proceedings of ACL 2018, System Demonstrations, pages 128–133, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
RETURNN as a Generic Flexible Neural Toolkit with Application to Translation and Speech Recognition (Zeyer et al., ACL 2018)
Copy Citation:
PDF:
https://aclanthology.org/P18-4022.pdf
Code
 rwth-i6/returnn +  additional community code