natural-language-processing-7
Task-Completion Dialogue Policy Learning
3260 papers • 126 benchmarks • 313 datasets
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Most implemented papers
Switch-based Active Deep Dyna-Q: Efficient Adaptive Planning for Task-Completion Dialogue Policy Learning
Yuexin Wu, Yiming Yang, Jianfeng Gao, Jingjing Liu, Xiujun Li•Sun Nov 18 2018
By combining switcher and active learning, the new framework named as Switch-based Active Deep Dyna-Q (Switch-DDQ), leads to significant improvement over DDQ and Q-learning baselines in both simulation and human evaluations.
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