Improving Spoken Dialogue Understanding Using Phonetic Mixture Models

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A Diversity-Promoting Objective Function for Neural Conversation Models (2015, background): “…heuristic-driven even where there is a statistical component (Levin et al., 2000; Oh and Rudnicky, 2000; Ratna-parkhi, 2002; Walker et al., 2003; Pieraccini et al., 2009; Young et al., 2010; Wang et al., 2011; Banchs and Li, 2012; Chen et al., 2013; Ameixa et al., 2014; Nio et al., 2014).”
A Persona-Based Neural Conversation Model (2016, methodology): “nsively on hand-coded rules, 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 a”
An empirical investigation of sparse log-linear models for improved dialogue act classification (2013, unknown): “Recently, methods that combine phonetic, word, and semantic representations [6, 7] have been shown to be useful.”