@inproceedings{drexler-etal-2014-wikipedia,
title = "A {W}ikipedia-based Corpus for Contextualized Machine Translation",
author = "Drexler, Jennifer and
Rastogi, Pushpendre and
Aguilar, Jacqueline and
Van Durme, Benjamin and
Post, Matt",
editor = "Calzolari, Nicoletta and
Choukri, Khalid and
Declerck, Thierry and
Loftsson, Hrafn and
Maegaard, Bente and
Mariani, Joseph and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
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/1217_Paper.pdf",
pages = "3593--3596",
abstract = "We describe a corpus for target-contextualized machine translation (MT), where the task is to improve the translation of source documents using language models built over presumably related documents in the target language. The idea presumes a situation where most of the information about a topic is in a foreign language, yet some related target-language information is known to exist. Our corpus comprises a set of curated English Wikipedia articles describing news events, along with (i) their Spanish counterparts and (ii) some of the Spanish source articles cited within them. In experiments, we translated these Spanish documents, treating the English articles as target-side context, and evaluate the effect on translation quality when including target-side language models built over this English context and interpolated with other, separately-derived language model data. We find that even under this simplistic baseline approach, we achieve significant improvements as measured by BLEU score.",
}
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<abstract>We describe a corpus for target-contextualized machine translation (MT), where the task is to improve the translation of source documents using language models built over presumably related documents in the target language. The idea presumes a situation where most of the information about a topic is in a foreign language, yet some related target-language information is known to exist. Our corpus comprises a set of curated English Wikipedia articles describing news events, along with (i) their Spanish counterparts and (ii) some of the Spanish source articles cited within them. In experiments, we translated these Spanish documents, treating the English articles as target-side context, and evaluate the effect on translation quality when including target-side language models built over this English context and interpolated with other, separately-derived language model data. We find that even under this simplistic baseline approach, we achieve significant improvements as measured by BLEU score.</abstract>
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%0 Conference Proceedings
%T A Wikipedia-based Corpus for Contextualized Machine Translation
%A Drexler, Jennifer
%A Rastogi, Pushpendre
%A Aguilar, Jacqueline
%A Van Durme, Benjamin
%A Post, Matt
%Y Calzolari, Nicoletta
%Y Choukri, Khalid
%Y Declerck, Thierry
%Y Loftsson, Hrafn
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Moreno, Asuncion
%Y Odijk, Jan
%Y Piperidis, Stelios
%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 drexler-etal-2014-wikipedia
%X We describe a corpus for target-contextualized machine translation (MT), where the task is to improve the translation of source documents using language models built over presumably related documents in the target language. The idea presumes a situation where most of the information about a topic is in a foreign language, yet some related target-language information is known to exist. Our corpus comprises a set of curated English Wikipedia articles describing news events, along with (i) their Spanish counterparts and (ii) some of the Spanish source articles cited within them. In experiments, we translated these Spanish documents, treating the English articles as target-side context, and evaluate the effect on translation quality when including target-side language models built over this English context and interpolated with other, separately-derived language model data. We find that even under this simplistic baseline approach, we achieve significant improvements as measured by BLEU score.
%U http://www.lrec-conf.org/proceedings/lrec2014/pdf/1217_Paper.pdf
%P 3593-3596
Markdown (Informal)
[A Wikipedia-based Corpus for Contextualized Machine Translation](http://www.lrec-conf.org/proceedings/lrec2014/pdf/1217_Paper.pdf) (Drexler et al., LREC 2014)
ACL
- Jennifer Drexler, Pushpendre Rastogi, Jacqueline Aguilar, Benjamin Van Durme, and Matt Post. 2014. A Wikipedia-based Corpus for Contextualized Machine Translation. In Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14), pages 3593–3596, Reykjavik, Iceland. European Language Resources Association (ELRA).