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What does the User Want? Information Gain for Hierarchical Dialogue Policy Optimisation.

, , , , , , , and . ASRU, page 969-976. IEEE, (2021)

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What does the User Want? Information Gain for Hierarchical Dialogue Policy Optimisation., , , , , , , and . ASRU, page 969-976. IEEE, (2021)EmoWOZ: A Large-Scale Corpus and Labelling Scheme for Emotion Recognition in Task-Oriented Dialogue Systems., , , , , , and . LREC, page 4096-4113. European Language Resources Association, (2022)Dense Rewards and Continual Reinforcement Learning for Task-oriented Dialogue Policies.. Heinrich-Heine-Universität, Düsseldorf, Germany, (2024)Dynamic Dialogue Policy for Continual Reinforcement Learning., , , , , , and . COLING, page 266-284. International Committee on Computational Linguistics, (2022)GenTUS: Simulating User Behaviour and Language in Task-oriented Dialogues with Generative Transformers., , , , , , and . SIGDIAL, page 270-282. Association for Computational Linguistics, (2022)Learning With an Open Horizon in Ever-Changing Dialogue Circumstances., , , , , , , , and . IEEE ACM Trans. Audio Speech Lang. Process., (2024)LAVA: Latent Action Spaces via Variational Auto-encoding for Dialogue Policy Optimization., , , , , , and . COLING, page 465-479. International Committee on Computational Linguistics, (2020)EmoUS: Simulating User Emotions in Task-Oriented Dialogues., , , , , , , , and . SIGIR, page 2526-2531. ACM, (2023)Dialogue Evaluation with Offline Reinforcement Learning., , , , , , and . SIGDIAL, page 478-489. Association for Computational Linguistics, (2022)From Chatter to Matter: Addressing Critical Steps of Emotion Recognition Learning in Task-oriented Dialogue., , , , , , , , and . SIGDIAL, page 85-103. Association for Computational Linguistics, (2023)