Trainable approaches to surface natural language generation and their application to conversational dialog systems

By Adwait Ratnaparkhi. 2002

53 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

A Knowledge-Grounded Neural Conversation Model (2017, background): “While these datasets (Ser-ban et al., 2015) have grown dramatically in size thanks in particular to social media (Ritter et al., 2011), this data is still very far from containing discussions of every entry in Wikipedia, Foursquare, Goodreads, or IMDB.”
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”
Deep Reinforcement Learning for Dialogue Generation (2016, background): “, 2014) models, and models that statistically learn generation rules (Oh and Rudnicky, 2000; Ratnaparkhi, 2002; Banchs and Li, 2012; Nio et al., 2014).”
Semantically Conditioned LSTM-based Natural Language Generation for Spoken Dialogue Systems (2015, background): “Ratnaparkhi (2002) later addressed some of the limitations of class-based LMs in the over-generation phase by using a mod-iﬁed generator based on a syntactic dependency tree.”
From captions to visual concepts and back (2014, methodology): “Finally, in contrast to [18, 25], we formulate the problem of generation as an optimization problem and search for the most likely sentence [37].”
A Persona-Based Neural Conversation Model (2016, background): “, 2011) or learning generation rules from a minimal set of authored rules or labels (Oh and Rudnicky, 2000; Ratnaparkhi, 2002; Banchs and Li, 2012; Ameixa et al., 2014; Nio et al., 2014; Chen et al., 2013).”
Neural Text Generation from Structured Data with Application to the Biography Domain (2016, methodology): “End-to-end approaches have combined sentence planning and surface realization by using explicitly aligned sentence/meaning pairs as training data (Ratnaparkhi, 2002; Wong and Mooney, 2007; Belz, 2008; Lu and Ng, 2011).”
A Survey of Available Corpora For Building Data-Driven Dialogue Systems: The Journal Version (2015, background): “, 2015), natural language generation (Langkilde and Knight, 1998; Oh and Rudnicky, 2000; Walker et al., 2002; Ratnaparkhi, 2002; Stent et al., 2004; Rieser and Lemon, 2010; Mairesse et al., 2010; Mairesse and Young, 2014; Wen et al., 2015; Sharma et al., 2016), and dialogue policy learning (Young et”
Stochastic Language Generation in Dialogue using Recurrent Neural Networks with Convolutional Sentence Reranking (2015, neutral): “Ratnaparkhi (2002) later addressed the limitations of n-gram LMs in the overgeneration phase by using a more sophisticated generator based on a syntactic dependency tree.”
Generating Text from Structured Data with Application to the Biography Domain (2016, methodology): “End-to-end approaches have combined sentence planning and surface realization by using explicitly aligned sentence/meaning pairs as training data (Ratnaparkhi, 2002; Wong and Mooney, 2007; Belz, 2008; Lu and Ng, 2011).”
Slot-consistent NLG for Task-oriented Dialogue Systems with Iterative Rectification Network (2020, background): “Ratnaparkhi (2002) address the limitations of n-gram language models by using more sophisticated syntactic dependency trees.”
Table-to-text Generation by Structure-aware Seq2seq Learning (2017, methodology): “Recent work combines content selection and surface realization in a uniﬁed framework (Ratnaparkhi 2002; Konstas and Lapata 2012; 2013; Sha et al. 2017) Our model borrowed the idea of representing a structured table by its ﬁeld and content information from (Le-bret, Grangier, and Auli 2016).”