Generating Text from Anonymised Structures

Emilie Colin, Claire Gardent


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.
Anthology ID:
W19-8614
Volume:
Proceedings of the 12th International Conference on Natural Language Generation
Month:
October–November
Year:
2019
Address:
Tokyo, Japan
Editors:
Kees van Deemter, Chenghua Lin, Hiroya Takamura
Venue:
INLG
SIG:
SIGGEN
Publisher:
Association for Computational Linguistics
Note:
Pages:
112–117
Language:
URL:
https://aclanthology.org/W19-8614
DOI:
10.18653/v1/W19-8614
Bibkey:
Cite (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.
Cite (Informal):
Generating Text from Anonymised Structures (Colin & Gardent, INLG 2019)
Copy Citation:
PDF:
https://aclanthology.org/W19-8614.pdf
Supplementary attachment:
 W19-8614.Supplementary_Attachment.pdf