6 papers
Emotionally Intelligent Task-oriented Dialogue Systems: Architecture, Representation, and Optimisation
Shutong Feng, Hsien-chin Lin, Nurul Lubis +5
Task-oriented dialogue (ToD) systems are designed to help users achieve specific goals through natural language interaction. While recent advances in large language models (LLMs) h…
Text-to-SQL Task-oriented Dialogue Ontology Construction
Renato Vukovic, Carel van Niekerk, Michael Heck +5
Large language models (LLMs) are widely used as general-purpose knowledge sources, but they rely on parametric knowledge, limiting explainability and trustworthiness. In task-orien…
Learning from Noisy Labels via Self-Taught On-the-Fly Meta Loss Rescaling
Michael Heck, Christian Geishauser, Nurul Lubis +6
Correct labels are indispensable for training effective machine learning models. However, creating high-quality labels is expensive, and even professionally labeled data contains e…
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…
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…