@inproceedings{castro-ferreira-etal-2018-neuralreg,
title = "{N}eural{REG}: An end-to-end approach to referring expression generation",
author = "Castro Ferreira, Thiago and
Moussallem, Diego and
K{\'a}d{\'a}r, {\'A}kos and
Wubben, Sander and
Krahmer, Emiel",
editor = "Gurevych, Iryna and
Miyao, Yusuke",
booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2018",
address = "Melbourne, Australia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P18-1182",
doi = "10.18653/v1/P18-1182",
pages = "1959--1969",
abstract = "Traditionally, Referring Expression Generation (REG) models first decide on the form and then on the content of references to discourse entities in text, typically relying on features such as salience and grammatical function. In this paper, we present a new approach (NeuralREG), relying on deep neural networks, which makes decisions about form and content in one go without explicit feature extraction. Using a delexicalized version of the WebNLG corpus, we show that the neural model substantially improves over two strong baselines.",
}
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<abstract>Traditionally, Referring Expression Generation (REG) models first decide on the form and then on the content of references to discourse entities in text, typically relying on features such as salience and grammatical function. In this paper, we present a new approach (NeuralREG), relying on deep neural networks, which makes decisions about form and content in one go without explicit feature extraction. Using a delexicalized version of the WebNLG corpus, we show that the neural model substantially improves over two strong baselines.</abstract>
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%0 Conference Proceedings
%T NeuralREG: An end-to-end approach to referring expression generation
%A Castro Ferreira, Thiago
%A Moussallem, Diego
%A Kádár, Ákos
%A Wubben, Sander
%A Krahmer, Emiel
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F castro-ferreira-etal-2018-neuralreg
%X Traditionally, Referring Expression Generation (REG) models first decide on the form and then on the content of references to discourse entities in text, typically relying on features such as salience and grammatical function. In this paper, we present a new approach (NeuralREG), relying on deep neural networks, which makes decisions about form and content in one go without explicit feature extraction. Using a delexicalized version of the WebNLG corpus, we show that the neural model substantially improves over two strong baselines.
%R 10.18653/v1/P18-1182
%U https://aclanthology.org/P18-1182
%U https://doi.org/10.18653/v1/P18-1182
%P 1959-1969
Markdown (Informal)
[NeuralREG: An end-to-end approach to referring expression generation](https://aclanthology.org/P18-1182) (Castro Ferreira et al., ACL 2018)
ACL
- Thiago Castro Ferreira, Diego Moussallem, Ákos Kádár, Sander Wubben, and Emiel Krahmer. 2018. NeuralREG: An end-to-end approach to referring expression generation. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1959–1969, Melbourne, Australia. Association for Computational Linguistics.