@inproceedings{logacheva-specia-2014-quality,
title = "A Quality-based Active Sample Selection Strategy for Statistical Machine Translation",
author = "Logacheva, Varvara and
Specia, Lucia",
booktitle = "Proceedings of the Ninth International Conference on Language Resources and Evaluation ({LREC}'14)",
month = may,
year = "2014",
address = "Reykjavik, Iceland",
publisher = "European Language Resources Association (ELRA)",
url = "http://www.lrec-conf.org/proceedings/lrec2014/pdf/658_Paper.pdf",
pages = "2690--2695",
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, i.e., 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.",
}
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%0 Conference Proceedings
%T A Quality-based Active Sample Selection Strategy for Statistical Machine Translation
%A Logacheva, Varvara
%A Specia, Lucia
%S Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC’14)
%D 2014
%8 may
%I European Language Resources Association (ELRA)
%C Reykjavik, Iceland
%F logacheva-specia-2014-quality
%X 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, i.e., 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.
%U http://www.lrec-conf.org/proceedings/lrec2014/pdf/658_Paper.pdf
%P 2690-2695
Markdown (Informal)
[A Quality-based Active Sample Selection Strategy for Statistical Machine Translation](http://www.lrec-conf.org/proceedings/lrec2014/pdf/658_Paper.pdf) (Logacheva & Specia, LREC 2014)
ACL