Name
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. |
Year | Venue | DocType |
|---|---|---|
2021 | INLG | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Steven Y. Feng | 1 | 0 | 1.35 |
| Jessica Huynh | 2 | 0 | 1.01 |
| Chaitanya Narisetty | 3 | 0 | 0.68 |
| Eduard Hovy | 4 | 0 | 0.34 |
| Varun Gangal | 5 | 3 | 3.76 |