Code for SAPPHIRE: Approaches for Enhanced Concept-to-Text Generation published at INLG 2021 (Best Long Paper). You can cite it as follows:
@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",
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",
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.",
}
Authors: Steven Y. Feng, Jessica Huynh, Chaitanya Prasad Narisetty, Eduard Hovy, Varun Gangal
Poster and other resources can be found here.
Note: inquiries should be directed to stevenyfeng@gmail.com or by opening an issue here.