@inproceedings{mathur-etal-2014-online,
title = "Online multi-user adaptive statistical machine translation",
author = "Mathur, Prashant and
Cettolo, Mauro and
Federico, Marcello and
de Souza, Jos{\'e} G.C.",
editor = "Al-Onaizan, Yaser and
Simard, Michel",
booktitle = "Proceedings of the 11th Conference of the Association for Machine Translation in the Americas: MT Researchers Track",
month = oct # " 22-26",
year = "2014",
address = "Vancouver, Canada",
publisher = "Association for Machine Translation in the Americas",
url = "https://aclanthology.org/2014.amta-researchers.12",
pages = "152--165",
abstract = "In this paper we investigate the problem of adapting a machine translation system to the feedback provided by multiple post-editors. It is well know that translators might have very different post-editing styles and that this variability hinders the application of online learning methods, which indeed assume a homogeneous source of adaptation data. We hence propose multi-task learning to leverage bias information from each single post-editors in order to constrain the evolution of the SMT system. A new framework for significance testing with sentence level metrics is described which shows that Multi-Task learning approaches outperforms existing online learning approaches, with significant gains of 1.24 and 1.88 TER score over a strong online adaptive baseline, on a test set of post-edits produced by four translators texts and on a popular benchmark with multiple references, respectively.",
}
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%0 Conference Proceedings
%T Online multi-user adaptive statistical machine translation
%A Mathur, Prashant
%A Cettolo, Mauro
%A Federico, Marcello
%A de Souza, José G.C.
%Y Al-Onaizan, Yaser
%Y Simard, Michel
%S Proceedings of the 11th Conference of the Association for Machine Translation in the Americas: MT Researchers Track
%D 2014
%8 oct 22 26
%I Association for Machine Translation in the Americas
%C Vancouver, Canada
%F mathur-etal-2014-online
%X In this paper we investigate the problem of adapting a machine translation system to the feedback provided by multiple post-editors. It is well know that translators might have very different post-editing styles and that this variability hinders the application of online learning methods, which indeed assume a homogeneous source of adaptation data. We hence propose multi-task learning to leverage bias information from each single post-editors in order to constrain the evolution of the SMT system. A new framework for significance testing with sentence level metrics is described which shows that Multi-Task learning approaches outperforms existing online learning approaches, with significant gains of 1.24 and 1.88 TER score over a strong online adaptive baseline, on a test set of post-edits produced by four translators texts and on a popular benchmark with multiple references, respectively.
%U https://aclanthology.org/2014.amta-researchers.12
%P 152-165
Markdown (Informal)
[Online multi-user adaptive statistical machine translation](https://aclanthology.org/2014.amta-researchers.12) (Mathur et al., AMTA 2014)
ACL
- Prashant Mathur, Mauro Cettolo, Marcello Federico, and José G.C. de Souza. 2014. Online multi-user adaptive statistical machine translation. In Proceedings of the 11th Conference of the Association for Machine Translation in the Americas: MT Researchers Track, pages 152–165, Vancouver, Canada. Association for Machine Translation in the Americas.