Shachar Mirkin, Laurent Becasier
The eleventh biennial conference of the Association for Machine Translation in the Americas (AMTA-2014), Vancouver, Canada, 22-26 October, 2014
Data selection is a common technique for adapting statistical translation models for a specific
domain, which has been shown to both improve translation quality and to reduce model size.
Selection relies on some in-domain data, of the same domain of the texts expected to be translated.
Selecting the sentence-pairs that are most similar to the in-domain data from a pool of
parallel texts has been shown to be effective; yet, this approach holds the risk of resulting in
a limited coverage, when necessary n-grams that do appear in the pool are less similar to indomain
data that is available in advance. Some methods select additional data based on the
actual text that needs to be translated. While useful, this is not always a practical scenario.
In this work we describe an extensive exploration of data selection techniques over Arabic to
French datasets, and propose methods to address both similarity and coverage considerations
Report number: