End-to-end LSTM-based dialog control optimized with supervised and reinforcement learning

arXiv: Computation and Language, Volume abs/1606.01269, 2016.

Cited by: 52|Bibtex|Views85
EI
Other Links: dblp.uni-trier.de|academic.microsoft.com|arxiv.org

Abstract:

This paper presents a model for end-to-end learning of task-oriented dialog systems. The main component of the model is a recurrent neural network (an LSTM), which maps from raw dialog history directly to a distribution over system actions. The LSTM automatically infers a representation of dialog history, which relieves the system develop...More

Code:

Data:

Full Text
Your rating :
0

 

Tags
Comments