@inproceedings{khayrallah-etal-2017-neural,
title = "Neural Lattice Search for Domain Adaptation in Machine Translation",
author = "Khayrallah, Huda and
Kumar, Gaurav and
Duh, Kevin and
Post, Matt and
Koehn, Philipp",
editor = "Kondrak, Greg and
Watanabe, Taro",
booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
month = nov,
year = "2017",
address = "Taipei, Taiwan",
publisher = "Asian Federation of Natural Language Processing",
url = "https://aclanthology.org/I17-2004",
pages = "20--25",
abstract = "Domain adaptation is a major challenge for neural machine translation (NMT). Given unknown words or new domains, NMT systems tend to generate fluent translations at the expense of adequacy. We present a stack-based lattice search algorithm for NMT and show that constraining its search space with lattices generated by phrase-based machine translation (PBMT) improves robustness. We report consistent BLEU score gains across four diverse domain adaptation tasks involving medical, IT, Koran, or subtitles texts.",
}
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<abstract>Domain adaptation is a major challenge for neural machine translation (NMT). Given unknown words or new domains, NMT systems tend to generate fluent translations at the expense of adequacy. We present a stack-based lattice search algorithm for NMT and show that constraining its search space with lattices generated by phrase-based machine translation (PBMT) improves robustness. We report consistent BLEU score gains across four diverse domain adaptation tasks involving medical, IT, Koran, or subtitles texts.</abstract>
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%0 Conference Proceedings
%T Neural Lattice Search for Domain Adaptation in Machine Translation
%A Khayrallah, Huda
%A Kumar, Gaurav
%A Duh, Kevin
%A Post, Matt
%A Koehn, Philipp
%Y Kondrak, Greg
%Y Watanabe, Taro
%S Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
%D 2017
%8 November
%I Asian Federation of Natural Language Processing
%C Taipei, Taiwan
%F khayrallah-etal-2017-neural
%X Domain adaptation is a major challenge for neural machine translation (NMT). Given unknown words or new domains, NMT systems tend to generate fluent translations at the expense of adequacy. We present a stack-based lattice search algorithm for NMT and show that constraining its search space with lattices generated by phrase-based machine translation (PBMT) improves robustness. We report consistent BLEU score gains across four diverse domain adaptation tasks involving medical, IT, Koran, or subtitles texts.
%U https://aclanthology.org/I17-2004
%P 20-25
Markdown (Informal)
[Neural Lattice Search for Domain Adaptation in Machine Translation](https://aclanthology.org/I17-2004) (Khayrallah et al., IJCNLP 2017)
ACL
- Huda Khayrallah, Gaurav Kumar, Kevin Duh, Matt Post, and Philipp Koehn. 2017. Neural Lattice Search for Domain Adaptation in Machine Translation. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 20–25, Taipei, Taiwan. Asian Federation of Natural Language Processing.