Interactive reinforcement learning for task-oriented dialogue management

By Pararth Shah, Dilek Z. Hakkani-Tür, Larry Heck. 2016

54 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Incremental Learning from Scratch for Task-Oriented Dialogue Systems (2019, background): “However, Shah et al. (2016) and Lipton et al. (2018) have reserved a few bits in the dialogue state for the domain extension.”
Dialogue Learning with Human Teaching and Feedback in End-to-End Trainable Task-Oriented Dialogue Systems (2018, background): “, 2016), and dialogue policy learning (Gasic and Young, 2014; Shah et al., 2016; Su et al., 2016, 2017).”
Bootstrapping a Neural Conversational Agent with Dialogue Self-Play, Crowdsourcing and On-Line Reinforcement Learning (2018, methodology): “In contrast, the recently proposed Machines Talking To Machines (M2M) approach (Shah et al. (2018)) is a functionality-driven process for training dialogue agents, which combines a dialogue self-play step and a crowd-sourcing step to obtain a higher quality of dialogues in terms of (i) diversity of ”
Deep Reinforcement Learning: An Overview (2017, unknown): “…and Weston (2016), Chen et al. (2016b), Dhingra et al. (2016), Fatemi et al. (2016), Li et al. (2016a), Lipton et al. (2016), Mesnil et al. (2015), Mo et al. (2016), Shah et al. (2016), Su et al. (2016a), Wen et al. (2015a), Williams and Zweig (2016), Yang et al. (2016), Zhao and Eskenazi (2016).”
Building a Conversational Agent Overnight with Dialogue Self-Play (2018, background): “…for training dialogue models from recent literature: either component-wise models for language understanding (Bapna et al. (2017)), state tracking (Rastogi et al. (2017)), dialogue policy (Shah et al. (2016)) and language generation (Nayak et al. (2017)), or end-to-end models (Wen et al. (2016)).”
Scalable multi-domain dialogue state tracking (2017, methodology): “…represents the system’s estimate of the user’s goal given the conversation history and is used (1) to make calls to an API, database or action provider based on the user’s preferences speciﬁed during the dialogue (2) as an input to the dialogue policy [1, 2] which predicts the next system response.”
A Teacher-Student Framework for Maintainable Dialog Manager (2018, background): “However, similar to Shah et al. (2016), the BBQ-Networks have reserved a few bits in the feature vector for new intents and slots.”
Beyond Turing: Intelligent Agents Centered on the User (2019, background): “Shah et al (2016) demonstrated the possibility of interactively adapting to the user’s response.”