@inproceedings{puduppully-etal-2019-university,
title = "{U}niversity of {E}dinburgh{'}s submission to the Document-level Generation and Translation Shared Task",
author = "Puduppully, Ratish and
Mallinson, Jonathan and
Lapata, Mirella",
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/D19-5630",
doi = "10.18653/v1/D19-5630",
pages = "268--272",
abstract = "The University of Edinburgh participated in all six tracks: NLG, MT, and MT+NLG with both English and German as targeted languages. For the NLG track, we submitted a multilingual system based on the Content Selection and Planning model of Puduppully et al (2019). For the MT track, we submitted Transformer-based Neural Machine Translation models, where out-of-domain parallel data was augmented with in-domain data extracted from monolingual corpora. Our MT+NLG systems disregard the structured input data and instead rely exclusively on the source summaries.",
}
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<abstract>The University of Edinburgh participated in all six tracks: NLG, MT, and MT+NLG with both English and German as targeted languages. For the NLG track, we submitted a multilingual system based on the Content Selection and Planning model of Puduppully et al (2019). For the MT track, we submitted Transformer-based Neural Machine Translation models, where out-of-domain parallel data was augmented with in-domain data extracted from monolingual corpora. Our MT+NLG systems disregard the structured input data and instead rely exclusively on the source summaries.</abstract>
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%0 Conference Proceedings
%T University of Edinburgh’s submission to the Document-level Generation and Translation Shared Task
%A Puduppully, Ratish
%A Mallinson, Jonathan
%A Lapata, Mirella
%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 puduppully-etal-2019-university
%X The University of Edinburgh participated in all six tracks: NLG, MT, and MT+NLG with both English and German as targeted languages. For the NLG track, we submitted a multilingual system based on the Content Selection and Planning model of Puduppully et al (2019). For the MT track, we submitted Transformer-based Neural Machine Translation models, where out-of-domain parallel data was augmented with in-domain data extracted from monolingual corpora. Our MT+NLG systems disregard the structured input data and instead rely exclusively on the source summaries.
%R 10.18653/v1/D19-5630
%U https://aclanthology.org/D19-5630
%U https://doi.org/10.18653/v1/D19-5630
%P 268-272
Markdown (Informal)
[University of Edinburgh’s submission to the Document-level Generation and Translation Shared Task](https://aclanthology.org/D19-5630) (Puduppully et al., NGT 2019)
ACL