@inproceedings{berard-etal-2019-machine,
title = "Machine Translation of Restaurant Reviews: New Corpus for Domain Adaptation and Robustness",
author = "Berard, Alexandre and
Calapodescu, Ioan and
Dymetman, Marc and
Roux, Claude and
Meunier, Jean-Luc and
Nikoulina, Vassilina",
editor = "Birch, Alexandra and
Finch, Andrew and
Hayashi, Hiroaki and
Konstas, Ioannis and
Luong, Thang and
Neubig, Graham and
Oda, Yusuke and
Sudoh, Katsuhito",
booktitle = "Proceedings of the 3rd Workshop on Neural Generation and Translation",
month = nov,
year = "2019",
address = "Hong Kong",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-5617",
doi = "10.18653/v1/D19-5617",
pages = "168--176",
abstract = "We share a French-English parallel corpus of Foursquare restaurant reviews, and define a new task to encourage research on Neural Machine Translation robustness and domain adaptation, in a real-world scenario where better-quality MT would be greatly beneficial. We discuss the challenges of such user-generated content, and train good baseline models that build upon the latest techniques for MT robustness. We also perform an extensive evaluation (automatic and human) that shows significant improvements over existing online systems. Finally, we propose task-specific metrics based on sentiment analysis or translation accuracy of domain-specific polysemous words.",
}
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%0 Conference Proceedings
%T Machine Translation of Restaurant Reviews: New Corpus for Domain Adaptation and Robustness
%A Berard, Alexandre
%A Calapodescu, Ioan
%A Dymetman, Marc
%A Roux, Claude
%A Meunier, Jean-Luc
%A Nikoulina, Vassilina
%Y Birch, Alexandra
%Y Finch, Andrew
%Y Hayashi, Hiroaki
%Y Konstas, Ioannis
%Y Luong, Thang
%Y Neubig, Graham
%Y Oda, Yusuke
%Y Sudoh, Katsuhito
%S Proceedings of the 3rd Workshop on Neural Generation and Translation
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong
%F berard-etal-2019-machine
%X We share a French-English parallel corpus of Foursquare restaurant reviews, and define a new task to encourage research on Neural Machine Translation robustness and domain adaptation, in a real-world scenario where better-quality MT would be greatly beneficial. We discuss the challenges of such user-generated content, and train good baseline models that build upon the latest techniques for MT robustness. We also perform an extensive evaluation (automatic and human) that shows significant improvements over existing online systems. Finally, we propose task-specific metrics based on sentiment analysis or translation accuracy of domain-specific polysemous words.
%R 10.18653/v1/D19-5617
%U https://aclanthology.org/D19-5617
%U https://doi.org/10.18653/v1/D19-5617
%P 168-176
Markdown (Informal)
[Machine Translation of Restaurant Reviews: New Corpus for Domain Adaptation and Robustness](https://aclanthology.org/D19-5617) (Berard et al., NGT 2019)
ACL