Dr. Dave Howcroft D.Howcroft@napier.ac.uk
Research Fellow
enunlg: a Python library for reproducible neural data-to-text experimentation
Howcroft, David M; Gkatzia, Dimitra
Authors
Dr Dimitra Gkatzia D.Gkatzia@napier.ac.uk
Associate Professor
Abstract
Over the past decade, a variety of neural ar-chitectures for data-to-text generation (NLG) have been proposed. However, each system typically has its own approach to pre-and post-processing and other implementation details. Diversity in implementations is desirable, but it also confounds attempts to compare model performance: are the differences due to the proposed architectures or are they a byprod-uct of the libraries used or a result of pre-and post-processing decisions made? To improve reproducibility, we re-implement several pre-Transformer neural models for data-to-text NLG within a single framework to facilitate direct comparisons of the models themselves and better understand the contributions of other design choices. We release our library at https: //github.com/NapierNLP/enunlg to serve as a baseline for ongoing work in this area including research on NLG for low-resource languages where transformers might not be optimal .
Citation
Howcroft, D. M., & Gkatzia, D. (2023). enunlg: a Python library for reproducible neural data-to-text experimentation. In Proceedings of the 16th International Natural Language Generation Conference: System Demonstrations (4-5)
Conference Name | 16th International Natural Language Generation Conference |
---|---|
Conference Location | Prague, Czechia |
Start Date | Sep 13, 2023 |
End Date | Sep 15, 2023 |
Acceptance Date | Jul 12, 2023 |
Online Publication Date | Sep 11, 2023 |
Publication Date | 2023 |
Deposit Date | Nov 15, 2023 |
Publicly Available Date | Nov 15, 2023 |
Publisher | Association for Computational Linguistics (ACL) |
Pages | 4-5 |
Book Title | Proceedings of the 16th International Natural Language Generation Conference: System Demonstrations |
ISBN | 979-8-89176-002-8 |
Public URL | http://researchrepository.napier.ac.uk/Output/3385911 |
Publisher URL | https://aclanthology.org/2023.inlg-demos.2/ |
Files
enunlg: a Python library for reproducible neural data-to-text experimentation
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Publisher Licence URL
http://creativecommons.org/licenses/by/4.0/
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