@inproceedings{farahnak-etal-2019-concordia,
title = "The Concordia {NLG} Surface Realizer at {SRST} 2019",
author = "Farahnak, Farhood and
Rafiee, Laya and
Kosseim, Leila and
Fevens, Thomas",
editor = "Mille, Simon and
Belz, Anja and
Bohnet, Bernd and
Graham, Yvette and
Wanner, Leo",
booktitle = "Proceedings of the 2nd Workshop on Multilingual Surface Realisation (MSR 2019)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-6308",
doi = "10.18653/v1/D19-6308",
pages = "63--67",
abstract = "This paper presents the model we developed for the shallow track of the 2019 NLG Surface Realization Shared Task. The model reconstructs sentences whose word order and word inflections were removed. We divided the problem into two sub-problems: reordering and inflecting. For the purpose of reordering, we used a pointer network integrated with a transformer model as its encoder-decoder modules. In order to generate the inflected forms of tokens, a Feed Forward Neural Network was employed.",
}
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<abstract>This paper presents the model we developed for the shallow track of the 2019 NLG Surface Realization Shared Task. The model reconstructs sentences whose word order and word inflections were removed. We divided the problem into two sub-problems: reordering and inflecting. For the purpose of reordering, we used a pointer network integrated with a transformer model as its encoder-decoder modules. In order to generate the inflected forms of tokens, a Feed Forward Neural Network was employed.</abstract>
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%0 Conference Proceedings
%T The Concordia NLG Surface Realizer at SRST 2019
%A Farahnak, Farhood
%A Rafiee, Laya
%A Kosseim, Leila
%A Fevens, Thomas
%Y Mille, Simon
%Y Belz, Anja
%Y Bohnet, Bernd
%Y Graham, Yvette
%Y Wanner, Leo
%S Proceedings of the 2nd Workshop on Multilingual Surface Realisation (MSR 2019)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F farahnak-etal-2019-concordia
%X This paper presents the model we developed for the shallow track of the 2019 NLG Surface Realization Shared Task. The model reconstructs sentences whose word order and word inflections were removed. We divided the problem into two sub-problems: reordering and inflecting. For the purpose of reordering, we used a pointer network integrated with a transformer model as its encoder-decoder modules. In order to generate the inflected forms of tokens, a Feed Forward Neural Network was employed.
%R 10.18653/v1/D19-6308
%U https://aclanthology.org/D19-6308
%U https://doi.org/10.18653/v1/D19-6308
%P 63-67
Markdown (Informal)
[The Concordia NLG Surface Realizer at SRST 2019](https://aclanthology.org/D19-6308) (Farahnak et al., 2019)
ACL
- Farhood Farahnak, Laya Rafiee, Leila Kosseim, and Thomas Fevens. 2019. The Concordia NLG Surface Realizer at SRST 2019. In Proceedings of the 2nd Workshop on Multilingual Surface Realisation (MSR 2019), pages 63–67, Hong Kong, China. Association for Computational Linguistics.