16 citations · 17 across the 4 of their papers we have counts for
6 papers
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…
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…
Out-of-Task Training for Dialog State Tracking Models
Michael Heck, Carel van Niekerk, Nurul Lubis +4
Dialog state tracking (DST) suffers from severe data sparsity. While many natural language processing (NLP) tasks benefit from transfer learning and multi-task learning, in dialog…
LAVA: Latent Action Spaces via Variational Auto-encoding for Dialogue Policy Optimization
Nurul Lubis, Christian Geishauser, Michael Heck +4
Reinforcement learning (RL) can enable task-oriented dialogue systems to steer the conversation towards successful task completion. In an end-to-end setting, a response can be cons…
Knowing What You Know: Calibrating Dialogue Belief State Distributions via Ensembles
Carel van Niekerk, Michael Heck, Christian Geishauser +4
The ability to accurately track what happens during a conversation is essential for the performance of a dialogue system. Current state-of-the-art multi-domain dialogue state track…
TripPy: A Triple Copy Strategy for Value Independent Neural Dialog State Tracking
Michael Heck, Carel van Niekerk, Nurul Lubis +4
Task-oriented dialog systems rely on dialog state tracking (DST) to monitor the user's goal during the course of an interaction. Multi-domain and open-vocabulary settings complicat…