@inproceedings{bourdaillet-langlais-2012-identifying,
title = "Identifying Infrequent Translations by Aligning Non Parallel Sentences",
author = "Bourdaillet, Julien and
Langlais, Philippe",
booktitle = "Proceedings of the 10th Conference of the Association for Machine Translation in the Americas: Research Papers",
month = oct # " 28-" # nov # " 1",
year = "2012",
address = "San Diego, California, USA",
publisher = "Association for Machine Translation in the Americas",
url = "https://aclanthology.org/2012.amta-papers.2",
abstract = "Aligning a sequence of words to one of its infrequent translations is a difficult task. We propose a simple and original solution to this problem that yields to significant gains over a state-of-the-art transpotting task. Our approach consists in aligning non parallel sentences from the training data in order to reinforce online the alignment models. We show that using only a few pairs of non parallel sentences allows to improve significantly the alignment of infrequent translations.",
}
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<abstract>Aligning a sequence of words to one of its infrequent translations is a difficult task. We propose a simple and original solution to this problem that yields to significant gains over a state-of-the-art transpotting task. Our approach consists in aligning non parallel sentences from the training data in order to reinforce online the alignment models. We show that using only a few pairs of non parallel sentences allows to improve significantly the alignment of infrequent translations.</abstract>
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%0 Conference Proceedings
%T Identifying Infrequent Translations by Aligning Non Parallel Sentences
%A Bourdaillet, Julien
%A Langlais, Philippe
%S Proceedings of the 10th Conference of the Association for Machine Translation in the Americas: Research Papers
%D 2012
%8 oct 28 nov 1
%I Association for Machine Translation in the Americas
%C San Diego, California, USA
%F bourdaillet-langlais-2012-identifying
%X Aligning a sequence of words to one of its infrequent translations is a difficult task. We propose a simple and original solution to this problem that yields to significant gains over a state-of-the-art transpotting task. Our approach consists in aligning non parallel sentences from the training data in order to reinforce online the alignment models. We show that using only a few pairs of non parallel sentences allows to improve significantly the alignment of infrequent translations.
%U https://aclanthology.org/2012.amta-papers.2
Markdown (Informal)
[Identifying Infrequent Translations by Aligning Non Parallel Sentences](https://aclanthology.org/2012.amta-papers.2) (Bourdaillet & Langlais, AMTA 2012)
ACL