Abstract
In this paper we propose a cascading framework for optimizing online learning in machine translation for a computer assisted translation scenario. With the use of online learning, several hyperparameters associated with the learning algorithm are introduced. The number of iterations of online learning can affect the translation quality as well. We discuss these issues and propose a few approaches to optimize the hyperparameters and to find the number of iterations required for online learning. We experimentally show that optimizing hyperparameters and number of iterations in online learning yields consistent improvement against baseline results.- Anthology ID:
- 2014.amta-workshop.4
- Volume:
- Workshop on interactive and adaptive machine translation
- Month:
- October 22
- Year:
- 2014
- Address:
- Vancouver, Canada
- Editors:
- Francisco Casacuberta, Marcello Federico, Philipp Koehn
- Venue:
- AMTA
- SIG:
- Publisher:
- Association for Machine Translation in the Americas
- Note:
- Pages:
- 32–41
- Language:
- URL:
- https://aclanthology.org/2014.amta-workshop.4
- DOI:
- Cite (ACL):
- Prashant Mathur and Mauro Cettolo. 2014. Optimized MT online learning in computer assisted translation. In Workshop on interactive and adaptive machine translation, pages 32–41, Vancouver, Canada. Association for Machine Translation in the Americas.
- Cite (Informal):
- Optimized MT online learning in computer assisted translation (Mathur & Cettolo, AMTA 2014)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2014.amta-workshop.4.pdf