Name
Abstract | ||
|---|---|---|
We propose a simple and effective modeling framework for controlled generation of multiple, diverse outputs. We focus on the setting of generating the next sentence of a story given its context. As controllable dimensions, we consider several sentence attributes, including sentiment, length, predicates, frames, and automatically-induced clusters. Our empirical results demonstrate: (1) our framework is accurate in terms of generating outputs that match the target control values; (2) our model yields increased maximum metric scores compared to standard n-best list generation via beam search; (3) controlling generation with semantic frames leads to a stronger combination of diversity and quality than other control variables as measured by automatic metrics. We also conduct a human evaluation to assess the utility of providing multiple suggestions for creative writing, demonstrating promising results for the potential of controllable, diverse generation in a collaborative writing system. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.18653/v1/D19-5605 | NGT@EMNLP-IJCNLP |
DocType | Volume | Citations |
Conference | D19-56 | 1 |
PageRank | References | Authors |
0.36 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lifu Tu | 1 | 2 | 1.39 |
| Xiaoan Ding | 2 | 4 | 2.10 |
| Dong Yu | 3 | 6264 | 475.73 |
| Kevin Gimpel | 4 | 1545 | 79.71 |