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R-norm: improving inter-speaker variability modelling at the score level via regression score normalisation.

, , and . INTERSPEECH, page 3117-3121. ISCA, (2013)

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Policy committee for adaptation in multi-domain spoken dialogue systems., , , , , and . ASRU, page 806-812. IEEE, (2015)On-line Active Reward Learning for Policy Optimisation in Spoken Dialogue Systems., , , , , , , and . ACL (1), The Association for Computer Linguistics, (2016)Continuously Learning Neural Dialogue Management., , , , , , , and . CoRR, (2016)Multi-domain Neural Network Language Generation for Spoken Dialogue Systems., , , , , , and . HLT-NAACL, page 120-129. The Association for Computational Linguistics, (2016)Reward Shaping with Recurrent Neural Networks for Speeding up On-Line Policy Learning in Spoken Dialogue Systems., , , , , and . SIGDIAL Conference, page 417-421. The Association for Computer Linguistics, (2015)Non-Autoregressive Text Generation with Pre-trained Language Models., , , , , , and . EACL, page 234-243. Association for Computational Linguistics, (2021)Multi-domain dialogue success classifiers for policy training., , , , , and . ASRU, page 763-770. IEEE, (2015)Learning from Real Users: Rating Dialogue Success with Neural Networks for Reinforcement Learning in Spoken Dialogue Systems., , , , , , and . CoRR, (2015)A Network-based End-to-End Trainable Task-oriented Dialogue System., , , , , , , and . CoRR, (2016)Stochastic Language Generation in Dialogue using Recurrent Neural Networks with Convolutional Sentence Reranking., , , , , , and . SIGDIAL Conference, page 275-284. The Association for Computer Linguistics, (2015)