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Policy committee for adaptation in multi-domain spoken dialogue systems., , , , , and . ASRU, page 806-812. IEEE, (2015)Proceedings of the 21st Annual Workshop of the Australasian Language Technology Association., , , , , , , and . ALTA, Association for Computational Linguistics, (2023)TOAD: Task-Oriented Automatic Dialogs with Diverse Response Styles., , , and . CoRR, (2024)On-line Active Reward Learning for Policy Optimisation in Spoken Dialogue Systems., , , , , , , and . ACL (1), The Association for Computer Linguistics, (2016)A Generative Model for Joint Natural Language Understanding and Generation., , , and . ACL, page 1795-1807. Association for Computational Linguistics, (2020)Languages You Know Influence Those You Learn: Impact of Language Characteristics on Multi-Lingual Text-to-Text Transfer., , , , , and . TL4NLP, volume 203 of Proceedings of Machine Learning Research, page 88-102. PMLR, (2022)Counter-fitting Word Vectors to Linguistic Constraints., , , , , , , , and . HLT-NAACL, page 142-148. The Association for Computational Linguistics, (2016)Semantically Conditioned LSTM-based Natural Language Generation for Spoken Dialogue Systems., , , , , and . EMNLP, page 1711-1721. The Association for Computational Linguistics, (2015)Learning from real users: rating dialogue success with neural networks for reinforcement learning in spoken dialogue systems., , , , , , and . INTERSPEECH, page 2007-2011. ISCA, (2015)Prompting for a conversation: How to control a dialog model?, , and . CoRR, (2022)