POMDP-based dialogue manager adaptation to extended domains

By Milica Gasic, Catherine Breslin, Matthew Henderson, Dongho Kim, Martin Szummer, Blaise Thomson, Pirros Tsiakoulis, Susannah Young. 2013

91 citations recorded by the source corpus.

Subfields: Dialogue & Conversational AI

How researchers see this paper

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

In Dialogue & Conversational AI

Citations in the corpus

Dialogue Learning With Human-In-The-Loop (2016, neutral): “, 2009), POMDP models (Young et al., 2010; 2013; Gašic et al., 2013; 2014) and policy learning (Su et al.”
Deal or No Deal? End-to-End Learning of Negotiation Dialogues (2017, methodology): “RL has been widely used to improve dialogue managers, which manage transitions between dialogue states (Singh et al., 2002; Pietquin et al., 2011; Rieser and Lemon, 2011; Gašic et al., 2013; Fatemi et al., 2016).”
Sample-efficient Actor-Critic Reinforcement Learning with Supervised Data for Dialogue Management (2017, methodology): “Better initialisation of GPRL has been studied in the context of domain adaptation by specifying a GP prior or re-using an existing model which is then pre-trained for the new domain (Gašić et al., 2013).”
Personalizing a Dialogue System With Transfer Reinforcement Learning (2016, methodology): “…algorithm, “PriorSim” [6] in which for each target user, the policy from the most similar user in the source domain is used as a prior, “PriorAll” [6] in which for each target user, the dialogue policy trained on all the users in the source domain is used as a prior, and “All” where the policy is…”
Incremental on-line adaptation of POMDP-based dialogue managers to extended domains (2014, methodology): “The resulting mean of the Gaussian process posterior from Eq.”
I love your chain mail! Making knights smile in a fantasy game world: Open-domain goal-oriented dialogue agents (2019, background): “Typically, they used RL to improve dialogue managers, which manage transitions between dialogue states (Singh et al., 2002; Pietquin et al., 2011; Rieser & Lemon, 2011; Gasic et al., 2013; Fatemi et al., 2016).”
Fine Grained Knowledge Transfer for Personalized Task-oriented Dialogue Systems (2017, background): “Gaˇsic et al. (Gaˇsic et al. 2013; Gasic et al. 2014) used transfer learning in the dialogue management module to extend a dialogue system to handle previous unseen concepts.”
A survey on empathetic dialogue systems (2020, background): “Its goal is to extend a dialogue system to include a previously unseen concept and then adapt the existing dialogue management system to an extended one [108].”
Distributed dialogue policies for multi-domain statistical dialogue management (2015, methodology): “Here we use the Bayesian Update of Dialogue State (BUDS) dialogue model [12] and we use the same approach as in [13] for defining the kernel func”
Transfer Learning for User Adaptation in Spoken Dialogue Systems (2016, methodology): “Representation transfer [37] is an active research area: one can find studies on option based transfers [20, 30], which are dependant on hierarchical reinforcement learning [36], on Proto-value functions [29, 15], or on representation transfer for Gaussian Processes Reinforcement Learning [16, 17, 5”
Feudal Reinforcement Learning for Dialogue Management in Large Domains (2018, methodology): “Space abstraction, instead, has been successfully applied to dialogue tasks such as Dialogue State Tracking (DST) (Henderson et al., 2014b), and policy transfer between domains (Gaˇsi´c et al., 2013, 2015; Wang et al., 2015).”
Deep Transfer in Reinforcement Learning by Language Grounding (2017, unknown): “There have been a few attempts at model-based transfer for RL (Taylor et al., 2008; Nguyen et al., 2012; Gašic et al., 2013; Wang et al., 2015) but these methods either rely on hand-coded inter-task mappings for state and actions spaces or require significant interactions”