Natural Language Generation as Planning under Uncertainty for Spoken Dialogue Systems

2010 ◽  
pp. 105-120 ◽  
Author(s):  
Verena Rieser ◽  
Oliver Lemon
Keyword(s):  
Natural Language ◽  
Natural Language Generation ◽  
Dialogue Systems ◽  
Planning Under Uncertainty ◽  
Spoken Dialogue Systems ◽  
Spoken Dialogue ◽  
Language Generation

scholarly journals Variational Cross-domain Natural Language Generation for Spoken Dialogue Systems

2018 ◽  
Author(s):  
Bo-Hsiang Tseng ◽  
Florian Kreyssig ◽  
Paweł Budzianowski ◽  
Iñigo Casanueva ◽  
Yen-Chen Wu ◽  
...  
Keyword(s):  
Natural Language ◽  
Natural Language Generation ◽  
Dialogue Systems ◽  
Spoken Dialogue Systems ◽  
Spoken Dialogue ◽  
Language Generation ◽  
Cross Domain

scholarly journals Semantically Conditioned LSTM-based Natural Language Generation for Spoken Dialogue Systems

2015 ◽  
Author(s):  
Tsung-Hsien Wen ◽  
Milica Gasic ◽  
Nikola Mrkšić ◽  
Pei-Hao Su ◽  
David Vandyke ◽  
...  
Keyword(s):  
Natural Language ◽  
Natural Language Generation ◽  
Dialogue Systems ◽  
Spoken Dialogue Systems ◽  
Spoken Dialogue ◽  
Language Generation

scholarly journals Reinforcement adaptation of an attention-based neural natural language generator for spoken dialogue systems

2019 ◽  
Vol 10 (1) ◽  
pp. 1-19
Author(s):  
Matthieu Riou ◽  
Bassam Jabaian ◽  
Stéphane Huet ◽  
Fabrice Lefèvre
Keyword(s):  
Natural Language ◽  
Short Term Memory ◽  
Network Models ◽  
Dialogue Systems ◽  
Neural Network Models ◽  
Spoken Dialogue Systems ◽  
Spoken Dialogue ◽  
Automatic Transcription ◽  
Long Short Term Memory ◽  
Expected Payoffs

Following some recent propositions to handle natural language generation in spoken dialogue systems with long short-term memory recurrent neural network models~\citep{Wen2016a} we first investigate a variant thereof with the objective of a better integration of the attention subnetwork. Then our next objective is to propose and evaluate a framework to adapt the NLG module online through direct interactions with the users. When doing so the basic way is to ask the user to utter an alternative sentence to express a particular dialogue act. But then the system has to decide between using an automatic transcription or to ask for a manual transcription. To do so a reinforcement learning approach based on an adversarial bandit scheme is retained. We show that by defining appropriately the rewards as a linear combination of expected payoffs and costs of acquiring the new data provided by the user, a system design can balance between improving the system's performance towards a better match with the user's preferences and the burden associated with it. Then the actual benefits of this system is assessed with a human evaluation, showing that the addition of more diverse utterances allows to produce sentences more satisfying for the user.

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