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• W4387430425 abstract "Pre-trained conversation models (PCMs) have achieved promising progress in recent years. However, existing PCMs for Task-oriented dialog (TOD) are insufficient for capturing the sequential nature of the TOD-related tasks, as well as for learning dialog policy information. To alleviate these problems, this paper proposes a task-progressive PCM with two policy-aware pre-training tasks. The model is pre-trained through three stages where TOD-related tasks are progressively employed according to the task logic of the TOD system. A global policy consistency task is designed to capture the multi-turn dialog policy sequential relation, and an act-based contrastive learning task is designed to capture similarities among samples with the same dialog policy. Our model achieves better results on both MultiWOZ and In-Car end-to-end dialog modeling benchmarks with only 18% parameters and 25% pre-training data compared to the previous state-of-the-art PCM, GALAXY. We make our code and data publicly available ( https://github.com/lucenzhong/TPLD )." @default.
• W4387430425 created "2023-10-08" @default.
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• W4387430425 date "2023-01-01" @default.
• W4387430425 modified "2023-10-09" @default.
• W4387430425 title "A Task-Oriented Dialog Model with Task-Progressive and Policy-Aware Pre-training" @default.
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• W4387430425 doi "https://doi.org/10.1007/978-3-031-44693-1_1" @default.
• W4387430425 hasPublicationYear "2023" @default.
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