@inproceedings{hasler-etal-2014-combining,
title = "Combining domain and topic adaptation for {SMT}",
author = "Hasler, Eva and
Haddow, Barry and
Koehn, Philipp",
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.11",
pages = "139--151",
abstract = "Recent years have seen increased interest in adapting translation models to test domains that are known in advance as well as using latent topic representations to adapt to unknown test domains. However, the relationship between domains and latent topics is still somewhat unclear and topic adaptation approaches typically do not make use of domain knowledge in the training data. We show empirically that combining domain and topic adaptation approaches can be beneficial and that topic representations can be used to predict the domain of a test document. Our best combined model yields gains of up to 0.82 BLEU over a domain-adapted translation system and up to 1.67 BLEU over an unadapted system, measured on the stronger of two training conditions.",
}
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%0 Conference Proceedings
%T Combining domain and topic adaptation for SMT
%A Hasler, Eva
%A Haddow, Barry
%A Koehn, Philipp
%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 hasler-etal-2014-combining
%X Recent years have seen increased interest in adapting translation models to test domains that are known in advance as well as using latent topic representations to adapt to unknown test domains. However, the relationship between domains and latent topics is still somewhat unclear and topic adaptation approaches typically do not make use of domain knowledge in the training data. We show empirically that combining domain and topic adaptation approaches can be beneficial and that topic representations can be used to predict the domain of a test document. Our best combined model yields gains of up to 0.82 BLEU over a domain-adapted translation system and up to 1.67 BLEU over an unadapted system, measured on the stronger of two training conditions.
%U https://aclanthology.org/2014.amta-researchers.11
%P 139-151
Markdown (Informal)
[Combining domain and topic adaptation for SMT](https://aclanthology.org/2014.amta-researchers.11) (Hasler et al., AMTA 2014)
ACL
- Eva Hasler, Barry Haddow, and Philipp Koehn. 2014. Combining domain and topic adaptation for SMT. In Proceedings of the 11th Conference of the Association for Machine Translation in the Americas: MT Researchers Track, pages 139–151, Vancouver, Canada. Association for Machine Translation in the Americas.