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Knowing What You Know: Calibrating Dialogue Belief State Distributions via Ensembles.

, , , , , , and . EMNLP (Findings), volume EMNLP 2020 of Findings of ACL, page 3096-3102. Association for Computational Linguistics, (2020)

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Eliciting Positive Emotional Impact in Dialogue Response Selection., , , and . IWSDS, volume 510 of Lecture Notes in Electrical Engineering, page 135-148. Springer, (2017)Dynamic Dialogue Policy for Continual Reinforcement Learning., , , , , , and . COLING, page 266-284. International Committee on Computational Linguistics, (2022)Processing negative emotions through social communication: Multimodal database construction and analysis., , , , and . ACII, page 79-85. IEEE Computer Society, (2017)GenTUS: Simulating User Behaviour and Language in Task-oriented Dialogues with Generative Transformers., , , , , , and . SIGDIAL, page 270-282. Association for Computational Linguistics, (2022)Optimizing Neural Response Generator with Emotional Impact Information., , , and . SLT, page 876-883. IEEE, (2018)Construction of Spontaneous Emotion Corpus from Indonesian TV Talk Shows and Its Application on Multimodal Emotion Recognition., , , , and . IEICE Trans. Inf. Syst., 101-D (8): 2092-2100 (2018)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)A study of social-affective communication: Automatic prediction of emotion triggers and responses in television talk shows., , , , , and . ASRU, page 777-783. IEEE, (2015)ConvLab-3: A Flexible Dialogue System Toolkit Based on a Unified Data Format., , , , , , , , , and 3 other author(s). CoRR, (2022)