Name
Abstract | ||
|---|---|---|
To facilitate zero-shot generalization in taskoriented dialog, this paper proposes Language Models as Data (LAD). LAD is a paradigm for creating diverse and accurate synthetic data which conveys the necessary structural constraints and can be used to train a downstream neural dialog model. LAD leverages GPT-3 to induce linguistic diversity. LAD achieves significant performance gains in zero-shot settings on intent prediction (+15%), slot filling (+31.4 F-1) and next action prediction (+11 F1). Furthermore, an interactive human evaluation shows that training with LAD is competitive with training on human dialogs. LAD is open-sourced, with the code and data available at https://github.com/Shikib/lad. |
Year | Venue | DocType |
|---|---|---|
2022 | SIGdial Meetings (SIGDIAL) | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shikib Mehri | 1 | 6 | 3.50 |
| Yasemin Altun | 2 | 0 | 0.68 |
| Maxine Eskenazi | 3 | 0 | 0.68 |