تقرير
LAD: Language Models as Data for Zero-Shot Dialog
| العنوان: | LAD: Language Models as Data for Zero-Shot Dialog |
|---|---|
| المؤلفون: | Mehri, Shikib, Altun, Yasemin, Eskenazi, Maxine |
| سنة النشر: | 2022 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computation and Language, Computer Science - Artificial Intelligence |
| الوصف: | 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. Comment: Accepted as a long paper to SIGDial 2022 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2207.14393 |
| رقم الأكسشن: | edsarx.2207.14393 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |