LAD: Language Models As Data for Zero-Shot Dialog
SIGDIAL Conferences(2022)
Abstract
To facilitate zero-shot generalization in task-oriented 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 (+10 F-1). Furthermore, an interactive human evaluation shows that training with LAD is competitive with training on human dialogs.
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Key words
Spoken Dialogue Systems,Topic Modeling,Dialog Management,Reinforcement Learning,Natural Language Generation
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