14 citations · 43 across the 18 of their papers we have counts for
4 papers · 1 filter
Dialogue Evaluation with Offline Reinforcement Learning
Nurul Lubis, Christian Geishauser, Hsien-Chin Lin +4
Task-oriented dialogue systems aim to fulfill user goals through natural language interactions. They are ideally evaluated with human users, which however is unattainable to do at…
GenTUS: Simulating User Behaviour and Language in Task-oriented Dialogues with Generative Transformers
Hsien-Chin Lin, Christian Geishauser, Shutong Feng +4
User simulators (USs) are commonly used to train task-oriented dialogue systems (DSs) via reinforcement learning. The interactions often take place on semantic level for efficiency…
Dynamic Dialogue Policy for Continual Reinforcement Learning
Christian Geishauser, Carel van Niekerk, Nurul Lubis +4
Continual learning is one of the key components of human learning and a necessary requirement of artificial intelligence. As dialogue can potentially span infinitely many topics an…
Robust Dialogue State Tracking with Weak Supervision and Sparse Data
Michael Heck, Nurul Lubis, Carel van Niekerk +4
Generalising dialogue state tracking (DST) to new data is especially challenging due to the strong reliance on abundant and fine-grained supervision during training. Sample sparsit…