139 citations · 291 across the 6 of their papers we have counts for
6 papers
Task-Oriented Dialogue with In-Context Learning
Tom Bocklisch, Thomas Werkmeister, Daksh Varshneya +1
We describe a system for building task-oriented dialogue systems combining the in-context learning abilities of large language models (LLMs) with the deterministic execution of bus…
DIET: Lightweight Language Understanding for Dialogue Systems
Tanja Bunk, Daksh Varshneya, Vladimir Vlasov +1
Large-scale pre-trained language models have shown impressive results on language understanding benchmarks like GLUE and SuperGLUE, improving considerably over other pre-training m…
Where is the context? -- A critique of recent dialogue datasets
Johannes E. M. Mosig, Vladimir Vlasov, Alan Nichol
Recent dialogue datasets like MultiWOZ 2.1 and Taskmaster-1 constitute some of the most challenging tasks for present-day dialogue models and, therefore, are widely used for system…
Dialogue Transformers
Vladimir Vlasov, Johannes E. M. Mosig, Alan Nichol
We introduce a dialogue policy based on a transformer architecture, where the self-attention mechanism operates over the sequence of dialogue turns. Recent work has used hierarchic…
Few-Shot Generalization Across Dialogue Tasks
Vladimir Vlasov, Akela Drissner-Schmid, Alan Nichol
Machine-learning based dialogue managers are able to learn complex behaviors in order to complete a task, but it is not straightforward to extend their capabilities to new domains.…
Rasa: Open Source Language Understanding and Dialogue Management
Tom Bocklisch, Joey Faulkner, Nick Pawlowski +1
We introduce a pair of tools, Rasa NLU and Rasa Core, which are open source python libraries for building conversational software. Their purpose is to make machine-learning based d…