Sisyphus, a Workflow Manager Designed for Machine Translation and Automatic Speech Recognition

Jan-Thorsten Peter, Eugen Beck, Hermann Ney


Abstract
Training and testing many possible parameters or model architectures of state-of-the-art machine translation or automatic speech recognition system is a cumbersome task. They usually require a long pipeline of commands reaching from pre-processing the training data to post-processing and evaluating the output.
Anthology ID:
D18-2015
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
Month:
November
Year:
2018
Address:
Brussels, Belgium
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
84–89
Language:
URL:
https://aclanthology.org/D18-2015
DOI:
10.18653/v1/D18-2015
Bibkey:
Cite (ACL):
Jan-Thorsten Peter, Eugen Beck, and Hermann Ney. 2018. Sisyphus, a Workflow Manager Designed for Machine Translation and Automatic Speech Recognition. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 84–89, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Sisyphus, a Workflow Manager Designed for Machine Translation and Automatic Speech Recognition (Peter et al., EMNLP 2018)
Copy Citation:
PDF:
https://aclanthology.org/D18-2015.pdf