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
- Editors:
- Eduardo Blanco, Wei Lu
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/add_acl24_videos/D18-2015.pdf