Learn what not to learn: Action elimination with deep reinforcement learning
Learning how to act when there are many available actions in each state is a challenging
task for Reinforcement Learning (RL) agents, especially when many of the actions are …
task for Reinforcement Learning (RL) agents, especially when many of the actions are …
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 …
Building a conversational agent overnight with dialogue self-play
We propose Machines Talking To Machines (M2M), a framework combining automation and
crowdsourcing to rapidly bootstrap end-to-end dialogue agents for goal-oriented dialogues …
crowdsourcing to rapidly bootstrap end-to-end dialogue agents for goal-oriented dialogues …
Dialogue learning with human teaching and feedback in end-to-end trainable task-oriented dialogue systems
In this work, we present a hybrid learning method for training task-oriented dialogue systems
through online user interactions. Popular methods for learning task-oriented dialogues …
through online user interactions. Popular methods for learning task-oriented dialogues …
Bootstrapping a neural conversational agent with dialogue self-play, crowdsourcing and on-line reinforcement learning
End-to-end neural models show great promise towards building conversational agents that
are trained from data and on-line experience using supervised and reinforcement learning …
are trained from data and on-line experience using supervised and reinforcement learning …
An end-to-end approach for handling unknown slot values in dialogue state tracking
P Xu, Q Hu - arXiv preprint arXiv:1805.01555, 2018 - arxiv.org
We highlight a practical yet rarely discussed problem in dialogue state tracking (DST),
namely handling unknown slot values. Previous approaches generally assume predefined …
namely handling unknown slot values. Previous approaches generally assume predefined …
Learning to prove theorems via interacting with proof assistants
Humans prove theorems by relying on substantial high-level reasoning and problem-
specific insights. Proof assistants offer a formalism that resembles human mathematical …
specific insights. Proof assistants offer a formalism that resembles human mathematical …
A survey on deep reinforcement learning for audio-based applications
Deep reinforcement learning (DRL) is poised to revolutionise the field of artificial intelligence
(AI) by endowing autonomous systems with high levels of understanding of the real world …
(AI) by endowing autonomous systems with high levels of understanding of the real world …
Transferable dialogue systems and user simulators
One of the difficulties in training dialogue systems is the lack of training data. We explore the
possibility of creating dialogue data through the interaction between a dialogue system and …
possibility of creating dialogue data through the interaction between a dialogue system and …
Airdialogue: An environment for goal-oriented dialogue research
Recent progress in dialogue generation has inspired a number of studies on dialogue
systems that are capable of accomplishing tasks through natural language interactions. A …
systems that are capable of accomplishing tasks through natural language interactions. A …