@inproceedings{feng-etal-2021-sapphire,
title = "{SAPPHIRE}: Approaches for Enhanced Concept-to-Text Generation",
author = "Feng, Steven Y. and
Huynh, Jessica and
Narisetty, Chaitanya Prasad and
Hovy, Eduard and
Gangal, Varun",
editor = "Belz, Anya and
Fan, Angela and
Reiter, Ehud and
Sripada, Yaji",
booktitle = "Proceedings of the 14th International Conference on Natural Language Generation",
month = aug,
year = "2021",
address = "Aberdeen, Scotland, UK",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.inlg-1.21/",
doi = "10.18653/v1/2021.inlg-1.21",
pages = "212--225",
abstract = "We motivate and propose a suite of simple but effective improvements for concept-to-text generation called SAPPHIRE: Set Augmentation and Post-hoc PHrase Infilling and REcombination. We demonstrate their effectiveness on generative commonsense reasoning, a.k.a. the CommonGen task, through experiments using both BART and T5 models. Through extensive automatic and human evaluation, we show that SAPPHIRE noticeably improves model performance. An in-depth qualitative analysis illustrates that SAPPHIRE effectively addresses many issues of the baseline model generations, including lack of commonsense, insufficient specificity, and poor fluency."
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<abstract>We motivate and propose a suite of simple but effective improvements for concept-to-text generation called SAPPHIRE: Set Augmentation and Post-hoc PHrase Infilling and REcombination. We demonstrate their effectiveness on generative commonsense reasoning, a.k.a. the CommonGen task, through experiments using both BART and T5 models. Through extensive automatic and human evaluation, we show that SAPPHIRE noticeably improves model performance. An in-depth qualitative analysis illustrates that SAPPHIRE effectively addresses many issues of the baseline model generations, including lack of commonsense, insufficient specificity, and poor fluency.</abstract>
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%0 Conference Proceedings
%T SAPPHIRE: Approaches for Enhanced Concept-to-Text Generation
%A Feng, Steven Y.
%A Huynh, Jessica
%A Narisetty, Chaitanya Prasad
%A Hovy, Eduard
%A Gangal, Varun
%Y Belz, Anya
%Y Fan, Angela
%Y Reiter, Ehud
%Y Sripada, Yaji
%S Proceedings of the 14th International Conference on Natural Language Generation
%D 2021
%8 August
%I Association for Computational Linguistics
%C Aberdeen, Scotland, UK
%F feng-etal-2021-sapphire
%X We motivate and propose a suite of simple but effective improvements for concept-to-text generation called SAPPHIRE: Set Augmentation and Post-hoc PHrase Infilling and REcombination. We demonstrate their effectiveness on generative commonsense reasoning, a.k.a. the CommonGen task, through experiments using both BART and T5 models. Through extensive automatic and human evaluation, we show that SAPPHIRE noticeably improves model performance. An in-depth qualitative analysis illustrates that SAPPHIRE effectively addresses many issues of the baseline model generations, including lack of commonsense, insufficient specificity, and poor fluency.
%R 10.18653/v1/2021.inlg-1.21
%U https://aclanthology.org/2021.inlg-1.21/
%U https://doi.org/10.18653/v1/2021.inlg-1.21
%P 212-225
Markdown (Informal)
[SAPPHIRE: Approaches for Enhanced Concept-to-Text Generation](https://aclanthology.org/2021.inlg-1.21/) (Feng et al., INLG 2021)
ACL
- Steven Y. Feng, Jessica Huynh, Chaitanya Prasad Narisetty, Eduard Hovy, and Varun Gangal. 2021. SAPPHIRE: Approaches for Enhanced Concept-to-Text Generation. In Proceedings of the 14th International Conference on Natural Language Generation, pages 212–225, Aberdeen, Scotland, UK. Association for Computational Linguistics.