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GALAXY: A Generative Pre-trained Model for Task-Oriented Dialog with Semi-supervised Learning and Explicit Policy Injection

AAAI 2022 · Label-efficient and robust learning › Semi-supervised learning · approach: Self-training theory · 71 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Pre-train with explicit dialog-act policy learning, consistency regularization, and gated unlabeled dataGALAXY · In-CarComb: from 104.95 to 107.45Helped
Pre-train with explicit dialog-act policy learning, consistency regularization, and gated unlabeled dataGALAXY · MultiWOZ2.0Comb: from 105.05 to 110.35Helped
Pre-train with explicit dialog-act policy learning, consistency regularization, and gated unlabeled dataGALAXY · MultiWOZ2.1Comb: from 105.25 to 110.76Helped
Add the unlabeled-data gating mechanism (ablation)GALAXY · MultiWOZ2.0Comb: 108.11 to 110.35Helped
Add the dialog-act prediction loss (ablation)GALAXY · MultiWOZ2.0Comb: 103.27 to 110.35Helped
Add consistency regularization on unlabeled dialogs (ablation)GALAXY · MultiWOZ2.0Comb: 107.27 to 110.35Helped
Add both dialog-act prediction and consistency regularization losses (ablation)GALAXY · MultiWOZ2.0Comb: 106.79 to 110.35Helped
Use dialog-policy-aware GALAXY pre-training instead of other conversation-model pre-trainingMultiWOZ2.0 · MultiWOZ2.0Comb: 106.53 to 110.35Helped
Use GALAXY rather than existing systems in the reported low-resource settingsMultiWOZ2.0 · 5%, 10%, 20%, and 50% training dataSuccessHelped

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