@inproceedings{colin-gardent-2019-generating,
title = "Generating Text from Anonymised Structures",
author = "Colin, Emilie and
Gardent, Claire",
editor = "van Deemter, Kees and
Lin, Chenghua and
Takamura, Hiroya",
booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
month = oct # "{--}" # nov,
year = "2019",
address = "Tokyo, Japan",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-8614",
doi = "10.18653/v1/W19-8614",
pages = "112--117",
abstract = "Surface realisation (SR) consists in generating a text from a meaning representations (MR). In this paper, we introduce a new parallel dataset of deep meaning representations (MR) and French sentences and we present a novel method for MR-to-text generation which seeks to generalise by abstracting away from lexical content. Most current work on natural language generation focuses on generating text that matches a reference using BLEU as evaluation criteria. In this paper, we additionally consider the model{'}s ability to reintroduce the function words that are absent from the deep input meaning representations. We show that our approach increases both BLEU score and the scores used to assess function words generation.",
}
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<abstract>Surface realisation (SR) consists in generating a text from a meaning representations (MR). In this paper, we introduce a new parallel dataset of deep meaning representations (MR) and French sentences and we present a novel method for MR-to-text generation which seeks to generalise by abstracting away from lexical content. Most current work on natural language generation focuses on generating text that matches a reference using BLEU as evaluation criteria. In this paper, we additionally consider the model’s ability to reintroduce the function words that are absent from the deep input meaning representations. We show that our approach increases both BLEU score and the scores used to assess function words generation.</abstract>
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%0 Conference Proceedings
%T Generating Text from Anonymised Structures
%A Colin, Emilie
%A Gardent, Claire
%Y van Deemter, Kees
%Y Lin, Chenghua
%Y Takamura, Hiroya
%S Proceedings of the 12th International Conference on Natural Language Generation
%D 2019
%8 oct–nov
%I Association for Computational Linguistics
%C Tokyo, Japan
%F colin-gardent-2019-generating
%X Surface realisation (SR) consists in generating a text from a meaning representations (MR). In this paper, we introduce a new parallel dataset of deep meaning representations (MR) and French sentences and we present a novel method for MR-to-text generation which seeks to generalise by abstracting away from lexical content. Most current work on natural language generation focuses on generating text that matches a reference using BLEU as evaluation criteria. In this paper, we additionally consider the model’s ability to reintroduce the function words that are absent from the deep input meaning representations. We show that our approach increases both BLEU score and the scores used to assess function words generation.
%R 10.18653/v1/W19-8614
%U https://aclanthology.org/W19-8614
%U https://doi.org/10.18653/v1/W19-8614
%P 112-117
Markdown (Informal)
[Generating Text from Anonymised Structures](https://aclanthology.org/W19-8614) (Colin & Gardent, INLG 2019)
ACL
- Emilie Colin and Claire Gardent. 2019. Generating Text from Anonymised Structures. In Proceedings of the 12th International Conference on Natural Language Generation, pages 112–117, Tokyo, Japan. Association for Computational Linguistics.