16 citations · 60 across the 15 of their papers we have counts for
4 papers · 1 filter
What Does The User Want? Information Gain for Hierarchical Dialogue Policy Optimisation
Christian Geishauser, Songbo Hu, Hsien-chin Lin +5
The dialogue management component of a task-oriented dialogue system is typically optimised via reinforcement learning (RL). Optimisation via RL is highly susceptible to sample ine…
Uncertainty Measures in Neural Belief Tracking and the Effects on Dialogue Policy Performance
Carel van Niekerk, Andrey Malinin, Christian Geishauser +5
The ability to identify and resolve uncertainty is crucial for the robustness of a dialogue system. Indeed, this has been confirmed empirically on systems that utilise Bayesian app…
EmoWOZ: A Large-Scale Corpus and Labelling Scheme for Emotion Recognition in Task-Oriented Dialogue Systems
Shutong Feng, Nurul Lubis, Christian Geishauser +4
The ability to recognise emotions lends a conversational artificial intelligence a human touch. While emotions in chit-chat dialogues have received substantial attention, emotions…
Domain-independent User Simulation with Transformers for Task-oriented Dialogue Systems
Hsien-chin Lin, Nurul Lubis, Songbo Hu +5
Dialogue policy optimisation via reinforcement learning requires a large number of training interactions, which makes learning with real users time consuming and expensive. Many se…