Are We There Yet? Research in Commercial Spoken Dialog Systems

By Roberto Pieraccini, D. Suendermann-Oeft, K. Dayanidhi, J. Liscombe. 2009

45 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

A Diversity-Promoting Objective Function for Neural Conversation Models (2015, background): “This stands in contrast to conventional dialog systems, which typically are template- or heuristicdriven even where there is a statistical component (Levin et al., 2000; Oh and Rudnicky, 2000; Ratnaparkhi, 2002; Walker et al., 2003; Pieraccini et al., 2009; Young et al., 2010; Wang et al., 2011; Ban”
Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models (2015, background): “puter game characters. Perhaps the most successful approach to goal driven systems has been to view the dialogue problem as a partially observable Markov decision process (POMDP) (Young et al., 2013; Pieraccini et al., 2009). Unfortunately, most deployed dialogue systems use hand-crafted features fo”
Deep Reinforcement Learning for Dialogue Generation (2016, methodology): “Efforts include statistical models such as Markov Decision Processes (MDPs) (Levin et al., 1997; Levin et al., 2000; Walker et al., 2003; Pieraccini et al., 2009), POMDP (Young et al.”
A Persona-Based Neural Conversation Model (2016, background): “…typically either building statistical models on top of heuristic rules or templates (Levin et al., 2000; Young et al., 2010; Walker et al., 2003; Pieraccini et al., 2009; Wang et al., 2011) or learning generation rules from a minimal set of authored rules or labels (Oh and Rudnicky, 2000;…”
A Deep Reinforcement Learning Chatbot (2017, background): “arkov decision process (MDP) appeared already in the 1990s for goal-oriented dialogue systems (Singh et al. 1999, 2002, Williams & Young 2007, Young et al. 2013, Paek 2006, Henderson et al. 2008, Pieraccini et al. 2009, Su et al. 2015). One line of research in this area has focused on learning d”
A Survey of Available Corpora For Building Data-Driven Dialogue Systems: The Journal Version (2015, background): “Research in this area started to take off during the mid 1990s, when researchers began to formulate dialogue as a sequential decision making problem based on Markov decision processes (Singh et al., 1999; Young et al., 2013; Paek, 2006; Pieraccini et al., 2009).”
Dialogue Learning With Human-In-The-Loop (2016, neutral): “Efforts include Markov Decision Processes (MDPs) (Levin et al., 1997; 2000; Walker et al., 2003; Pieraccini et al., 2009), POMDP models (Young et al.”
Hierarchical Neural Network Generative Models for Movie Dialogues (2015, background): “Perhaps the most successful approach to goal driven systems has been to view the dialogue problem as a partially observable Markov decision process (POMDP) (Young et al., 2013; Pieraccini et al., 2009).”
The Bottleneck Simulator: A Model-based Deep Reinforcement Learning Approach (2018, background): “Dialogue Systems : Numerous researchers have applied RL for training goal-oriented dialogue systems (Singh et al., 1999; Williams & Young, 2007; Pieraccini et al., 2009).”
IRIS: a Chat-oriented Dialogue System based on the Vector Space Model (2012, background): “…recent advances in other related language technologies such as speech recognition, discourse analysis and natural language understanding have made possible for dialogue systems to find practical applications that are commercially exploitable (Pieraccini et al. , 2009; Griol et al. , 2010).”