Pomdp-based statistical spoken dialog systems: A review
Statistical dialog systems (SDSs) are motivated by the need for a data-driven framework that
reduces the cost of laboriously handcrafting complex dialog managers and that provides …
reduces the cost of laboriously handcrafting complex dialog managers and that provides …
A survey of available corpora for building data-driven dialogue systems
During the past decade, several areas of speech and language understanding have
witnessed substantial breakthroughs from the use of data-driven models. In the area of …
witnessed substantial breakthroughs from the use of data-driven models. In the area of …
Towards end-to-end learning for dialog state tracking and management using deep reinforcement learning
T Zhao, M Eskenazi - arXiv preprint arXiv:1606.02560, 2016 - arxiv.org
This paper presents an end-to-end framework for task-oriented dialog systems using a
variant of Deep Recurrent Q-Networks (DRQN). The model is able to interface with a …
variant of Deep Recurrent Q-Networks (DRQN). The model is able to interface with a …
Deep dyna-q: Integrating planning for task-completion dialogue policy learning
Training a task-completion dialogue agent via reinforcement learning (RL) is costly because
it requires many interactions with real users. One common alternative is to use a user …
it requires many interactions with real users. One common alternative is to use a user …
Bbq-networks: Efficient exploration in deep reinforcement learning for task-oriented dialogue systems
We present a new algorithm that significantly improves the efficiency of exploration for deep
Q-learning agents in dialogue systems. Our agents explore via Thompson sampling …
Q-learning agents in dialogue systems. Our agents explore via Thompson sampling …
Unsupervised discrete sentence representation learning for interpretable neural dialog generation
The encoder-decoder dialog model is one of the most prominent methods used to build
dialog systems in complex domains. Yet it is limited because it cannot output interpretable …
dialog systems in complex domains. Yet it is limited because it cannot output interpretable …
[图书][B] Neural approaches to conversational information retrieval
A conversational information retrieval (CIR) system is an information retrieval (IR) system
with a conversational interface, which allows users to interact with the system to seek …
with a conversational interface, which allows users to interact with the system to seek …
Gaussian processes for pomdp-based dialogue manager optimization
A partially observable Markov decision process (POMDP) has been proposed as a dialog
model that enables automatic optimization of the dialog policy and provides robustness to …
model that enables automatic optimization of the dialog policy and provides robustness to …
Sample efficient deep reinforcement learning for dialogue systems with large action spaces
In spoken dialogue systems, we aim to deploy artificial intelligence to build automated
dialogue agents that can converse with humans. A part of this effort is the policy optimization …
dialogue agents that can converse with humans. A part of this effort is the policy optimization …
Policy networks with two-stage training for dialogue systems
In this paper, we propose to use deep policy networks which are trained with an advantage
actor-critic method for statistically optimised dialogue systems. First, we show that, on …
actor-critic method for statistically optimised dialogue systems. First, we show that, on …