Luke, I am Your Father: Dealing with Out-of-Domain Requests by Using Movies Subtitles

By David Ameixa, Luísa Coheur, Pedro Fialho, Paulo Quaresma. 2014

80 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Natural Language Processing, Dialogue & Conversational AI

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Citations in the corpus

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).”
Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models (2015, methodology): “On the other end of the spectrum are the non-goaldriven systems (Ritter, Cherry, and Dolan 2011; Banchs and Li 2012; Ameixa et al. 2014).”
A Persona-Based Neural Conversation Model (2016, methodology): “r 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; Ratnaparkhi, 2002; Banchs and Li, 2012; Ameixa et al., 2014; Nio et al., 2014; Chen et al., 2013). More recently (Wen et al., 2015) have use”
A Survey of Available Corpora For Building Data-Driven Dialogue Systems: The Journal Version (2015, methodology): “Similarly, Ameixa et al. (2014) also use an information retrieval system, but based on movie subtitles instead of movie scripts.”
The Octopus Approach to the Alexa Competition : A Deep Ensemble-based Socialbot (2017, methodology): “…and extracting responses from all Reddit datasets, • VHREDWashingtonPost trained on Reddit https://www.reddit.com/r/politics and extracting responses from user comments to WashingtonPost news articles, and • VHREDSubtitles using the movie subtitles dataset SubTle [Ameixa et al., 2014].”
Hierarchical Neural Network Generative Models for Movie Dialogues (2015, methodology): “On the other end of the spectrum are the nongoal driven systems (Ritter et al., 2011; Banchs and Li, 2012; Ameixa et al., 2014; Nio et al., 2014).”
Say What I Want: Towards the Dark Side of Neural Dialogue Models (2019, methodology): “Meanwhile, retrieval-based methods [4, 5, 6, 7], which search a suitable response from a repository given the query, are also adopted in many application scenarios.”
Multi-Turn Response Selection for Chatbots with Deep Attention Matching Network (2018, background): “Most modern chatbots are data-driven, either in a fashion of information-retrieval (Ji et al., 2014; Banchs and Li, 2012; Nio et al., 2014; Ameixa et al., 2014) or sequence-generation (Ritter et al., 2011).”
Lifelong Knowledge Learning in Rule-based Dialogue Systems (2020, methodology): “Early chatbots were mainly built with rules and retrieval models (Banchs and Li, 2012; Ameixa et al., 2014; Lowe et al., 2015; Serban et al., 2015).”
Lifelong and Interactive Learning of Factual Knowledge in Dialogues (2019, methodology): “Early such systems were built mainly based on rules and information retrieval techniques (Banchs and Li, 2012; Ameixa et al., 2014; Lowe et al., 2015; Serban et al., 2015).”
Towards a Continuous Knowledge Learning Engine for Chatbots (2018, methodology): “Early such systems were mostly built using markup languages such as AIML(1), handcrafted conversation generation rules, and/or information retrieval techniques (Banchs and Li, 2012; Ameixa et al., 2014; Lowe et al., 2015; Serban et al., 2015).”