170 citations · 669 across the 14 of their papers we have counts for
5 papers · 1 filter
Semantically Conditioned LSTM-based Natural Language Generation for Spoken Dialogue Systems
Tsung-Hsien Wen, Milica Gasic, Nikola Mrksic +3
Natural language generation (NLG) is a critical component of spoken dialogue and it has a significant impact both on usability and perceived quality. Most NLG systems in common use…
Reward Shaping with Recurrent Neural Networks for Speeding up On-Line Policy Learning in Spoken Dialogue Systems
Pei-Hao Su, David Vandyke, Milica Gasic +3
Statistical spoken dialogue systems have the attractive property of being able to be optimised from data via interactions with real users. However in the reinforcement learning par…
Learning from Real Users: Rating Dialogue Success with Neural Networks for Reinforcement Learning in Spoken Dialogue Systems
Pei-Hao Su, David Vandyke, Milica Gasic +4
To train a statistical spoken dialogue system (SDS) it is essential that an accurate method for measuring task success is available. To date training has relied on presenting a tas…
Stochastic Language Generation in Dialogue using Recurrent Neural Networks with Convolutional Sentence Reranking
Tsung-Hsien Wen, Milica Gasic, Dongho Kim +4
The natural language generation (NLG) component of a spoken dialogue system (SDS) usually needs a substantial amount of handcrafting or a well-labeled dataset to be trained on. The…
Multi-domain Dialog State Tracking using Recurrent Neural Networks
Nikola Mrkšić, Diarmuid Ó Séaghdha, Blaise Thomson +5
Dialog state tracking is a key component of many modern dialog systems, most of which are designed with a single, well-defined domain in mind. This paper shows that dialog data dra…