@inproceedings{clark-etal-2012-one,
title = "One System, Many Domains: Open-Domain Statistical Machine Translation via Feature Augmentation",
author = "Clark, Jonathan and
Lavie, Alon and
Dyer, Chris",
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.4",
abstract = "In this paper, we introduce a simple technique for incorporating domain information into a statistical machine translation system that significantly improves translation quality when test data comes from multiple domains. Our approach augments (conjoins) standard translation model and language model features with domain indicator features and requires only minimal modifications to the optimization and decoding procedures. We evaluate our method on two language pairs with varying numbers of domains, and observe significant improvements of up to 1.0 BLEU.",
}
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<abstract>In this paper, we introduce a simple technique for incorporating domain information into a statistical machine translation system that significantly improves translation quality when test data comes from multiple domains. Our approach augments (conjoins) standard translation model and language model features with domain indicator features and requires only minimal modifications to the optimization and decoding procedures. We evaluate our method on two language pairs with varying numbers of domains, and observe significant improvements of up to 1.0 BLEU.</abstract>
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%0 Conference Proceedings
%T One System, Many Domains: Open-Domain Statistical Machine Translation via Feature Augmentation
%A Clark, Jonathan
%A Lavie, Alon
%A Dyer, Chris
%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 clark-etal-2012-one
%X In this paper, we introduce a simple technique for incorporating domain information into a statistical machine translation system that significantly improves translation quality when test data comes from multiple domains. Our approach augments (conjoins) standard translation model and language model features with domain indicator features and requires only minimal modifications to the optimization and decoding procedures. We evaluate our method on two language pairs with varying numbers of domains, and observe significant improvements of up to 1.0 BLEU.
%U https://aclanthology.org/2012.amta-papers.4
Markdown (Informal)
[One System, Many Domains: Open-Domain Statistical Machine Translation via Feature Augmentation](https://aclanthology.org/2012.amta-papers.4) (Clark et al., AMTA 2012)
ACL