[PDF][PDF] A Quality-based Active Sample Selection Strategy for Statistical Machine Translation.

V Logacheva, L Specia - LREC, 2014 - lrec-conf.org
V Logacheva, L Specia
LREC, 2014lrec-conf.org
This paper presents a new active learning technique for machine translation based on
quality estimation of automatically translated sentences. It uses an error-driven strategy, ie, it
assumes that the more errors an automatically translated sentence contains, the more
informative it is for the translation system. Our approach is based on a quality estimation
technique which involves a wider range of features of the source text, automatic translation,
and machine translation system compared to previous work. In addition, we enhance the …
Abstract
This paper presents a new active learning technique for machine translation based on quality estimation of automatically translated sentences. It uses an error-driven strategy, ie, it assumes that the more errors an automatically translated sentence contains, the more informative it is for the translation system. Our approach is based on a quality estimation technique which involves a wider range of features of the source text, automatic translation, and machine translation system compared to previous work. In addition, we enhance the machine translation system training data with post-edited machine translations of the sentences selected, instead of simulating this using previously created reference translations. We found that re-training systems with additional post-edited data yields higher quality translations regardless of the selection strategy used. We relate this to the fact that post-editions tend to be closer to source sentences as compared to references, making the rule extraction process more reliable.
lrec-conf.org
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