Name
Title | ||
|---|---|---|
End-to-End Optimization of Task-Oriented Dialogue Model with Deep Reinforcement Learning. |
Abstract | ||
|---|---|---|
In this paper, we present a neural network based task-oriented dialogue system that can be optimized end-to-end with deep reinforcement learning (RL). The system is able to track dialogue state, interface with knowledge bases, and incorporate query results into agentu0027s responses to successfully complete task-oriented dialogues. Dialogue policy learning is conducted with a hybrid supervised and deep RL methods. We first train the dialogue agent in a supervised manner by learning directly from task-oriented dialogue corpora, and further optimize it with deep RL during its interaction with users. In the experiments on two different dialogue task domains, our model demonstrates robust performance in tracking dialogue state and producing reasonable system responses. We show that deep RL based optimization leads to significant improvement on task success rate and reduction in dialogue length comparing to supervised training model. We further show benefits of training task-oriented dialogue model end-to-end comparing to component-wise optimization with experiment results on dialogue simulations and human evaluations. |
Year | Venue | Field |
|---|---|---|
2017 | arXiv: Computation and Language | Policy learning,End-to-end principle,Computer science,Artificial intelligence,Supervised training,Artificial neural network,Task oriented,Machine learning,Reinforcement learning |
DocType | Volume | Citations |
Journal | abs/1711.10712 | 9 |
PageRank | References | Authors |
0.48 | 14 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bing Liu | 1 | 56 | 11.41 |
| Gokhan Tur | 2 | 931 | 83.35 |
| Dilek Hakkani-Tür | 3 | 1024 | 85.05 |
| Pararth Shah | 4 | 30 | 3.86 |
| Larry P. Heck | 5 | 1096 | 100.58 |