120 citations · 199 across the 7 of their papers we have counts for
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cs.AI2016★ 10 cited
Sample-efficient Deep Reinforcement Learning for Dialog Control
Kavosh Asadi, Jason D. Williams
Representing a dialog policy as a recurrent neural network (RNN) is attractive because it handles partial observability, infers a latent representation of state, and can be optimiz…
cs.CL2016★ 120 cited
End-to-end LSTM-based dialog control optimized with supervised and reinforcement learning
Jason D. Williams, Geoffrey Zweig
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 di…