Assessing quick update methods of statistical translation models

Shachar Mirkin, Nicola Cancedda


Abstract
The ability to quickly incorporate incoming training data into a running translation system is critical in a number of applications. Mechanisms based on incremental model update and the online EM algorithm hold the promise of achieving this objective in a principled way. Still, efficient tools for incremental training are yet to be available. In this paper we experiment with simple alternative solutions for interim model updates, within the popular Moses system. Short of updating the model in real time, such updates can execute in short timeframes even when operating on large models, and achieve a performance level close to, and in some cases exceeding, that of batch retraining.
Anthology ID:
2013.iwslt-papers.10
Volume:
Proceedings of the 10th International Workshop on Spoken Language Translation: Papers
Month:
December 5-6
Year:
2013
Address:
Heidelberg, Germany
Editor:
Joy Ying Zhang
Venue:
IWSLT
SIG:
SIGSLT
Publisher:
Note:
Pages:
Language:
URL:
https://aclanthology.org/2013.iwslt-papers.10
DOI:
Bibkey:
Cite (ACL):
Shachar Mirkin and Nicola Cancedda. 2013. Assessing quick update methods of statistical translation models. In Proceedings of the 10th International Workshop on Spoken Language Translation: Papers, Heidelberg, Germany.
Cite (Informal):
Assessing quick update methods of statistical translation models (Mirkin & Cancedda, IWSLT 2013)
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PDF:
https://aclanthology.org/2013.iwslt-papers.10.pdf