Chaitanya Prasad Narisetty
SAPPHIRE: Approaches for Enhanced Concept-to-Text Generation
Steven Y. Feng | Jessica Huynh | Chaitanya Prasad Narisetty | Eduard Hovy | Varun Gangal
Proceedings of the 14th International Conference on Natural Language Generation
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.