Developing Non-goal Dialog System Based on Examples of Drama Television

By Lasguido Nio, Sakriani Watiasri Sakti, Graham Neubig, Tomoki Toda, Mirna Adriani, Satoshi Nakamura. 2012

39 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Social Psychology, Natural Language Processing

How researchers see this paper

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

In Artificial Intelligence

1. Jianfeng Gao
2. Mohit Bansal
3. Jason Aaron Edward Weston
4. Kyunghyun Cho
5. Percy Liang
6. Yoshua Bengio
7. Graham Neubig
8. Yejin Choi

In Social Psychology

1. Jianfeng Gao
2. Michel Galley
3. Dan Jurafsky
4. Yoshua Bengio
5. Laurent Charlin
6. Peter Henderson

In Natural Language Processing

1. Jianfeng Gao
2. Jason Aaron Edward Weston
3. Mohit Bansal
4. Kyunghyun Cho
5. Michel Galley
6. Joelle Pineau
7. Yejin Choi
8. Graham Neubig

Citations in the corpus

• A Knowledge-Grounded Neural Conversation Model (2017, background): “In many respects, this second paradigm can be characterized as an extension of conventional dialog models with or without statistical modelling, e.g., (Oh and Rudnicky, 2000; Ratnaparkhi, 2002; Banchs and Li, 2012; Ameixa et al., 2014; Nio et al., 2014).”
• 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): “equire large corpora of annotated task-specific simulated conversations. 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). Most recently Sordoni et al. (2015b) and Shang et al. (2015) have drawn inspirati”
• 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).”
• 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).”
• A Survey of Available Corpora For Building Data-Driven Dialogue Systems: The Journal Version (2015, methodology): “For movie scripts, researchers have proposed the use of simple informationretrieval similarity measures, such as cosine similarity, to identify conversations (Nio et al., 2014a).”
• 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).”
• 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).”