@inproceedings{gong-etal-2012-towards,
title = "Towards contextual adaptation for any-text translation",
author = "Gong, Li and
Max, Aur{\'e}lien and
Yvon, Fran{\c{c}}ois",
booktitle = "Proceedings of the 9th International Workshop on Spoken Language Translation: Papers",
month = dec # " 6-7",
year = "2012",
address = "Hong Kong, Table of contents",
url = "https://aclanthology.org/2012.iwslt-papers.20/",
pages = "292--299",
abstract = "Adaptation for Machine Translation has been studied in a variety of ways, using an ideal scenario where the training data can be split into {\textquotedblright}out-of-domain{\textquotedblright} and {\textquotedblright}in-domain{\textquotedblright} corpora, on which the adaptation is based. In this paper, we consider a more realistic setting which does not assume the availability of any kind of {\textquotedblright}in-domain{\textquotedblright} data, hence the name {\textquotedblright}any-text translation{\textquotedblright}. In this context, we present a new approach to contextually adapt a translation model onthe-fly, and present several experimental results where this approach outperforms conventionaly trained baselines. We also present a document-level contrastive evaluation whose results can be easily interpreted, even by non-specialists."
}
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<abstract>Adaptation for Machine Translation has been studied in a variety of ways, using an ideal scenario where the training data can be split into ”out-of-domain” and ”in-domain” corpora, on which the adaptation is based. In this paper, we consider a more realistic setting which does not assume the availability of any kind of ”in-domain” data, hence the name ”any-text translation”. In this context, we present a new approach to contextually adapt a translation model onthe-fly, and present several experimental results where this approach outperforms conventionaly trained baselines. We also present a document-level contrastive evaluation whose results can be easily interpreted, even by non-specialists.</abstract>
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%0 Conference Proceedings
%T Towards contextual adaptation for any-text translation
%A Gong, Li
%A Max, Aurélien
%A Yvon, François
%S Proceedings of the 9th International Workshop on Spoken Language Translation: Papers
%D 2012
%8 dec 6 7
%C Hong Kong, Table of contents
%F gong-etal-2012-towards
%X Adaptation for Machine Translation has been studied in a variety of ways, using an ideal scenario where the training data can be split into ”out-of-domain” and ”in-domain” corpora, on which the adaptation is based. In this paper, we consider a more realistic setting which does not assume the availability of any kind of ”in-domain” data, hence the name ”any-text translation”. In this context, we present a new approach to contextually adapt a translation model onthe-fly, and present several experimental results where this approach outperforms conventionaly trained baselines. We also present a document-level contrastive evaluation whose results can be easily interpreted, even by non-specialists.
%U https://aclanthology.org/2012.iwslt-papers.20/
%P 292-299
Markdown (Informal)
[Towards contextual adaptation for any-text translation](https://aclanthology.org/2012.iwslt-papers.20/) (Gong et al., IWSLT 2012)
ACL