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Traditional neural language models tend to generate generic replies with poor logic and no emotion.
An affective model of interplay between emotions and learning: reengineering educational pedagogy-building a learning companion
B. Kort, R. Reilly, and R. W. Picard. 2002 · 2001
Earlier work this paper cites.
Latent dirichlet allocation
David M Blei, Andrew Y Ng, and Michael I Jordan. 2003 · 2003
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Constructing the affective lexicon ontology
L. Xu, H. Lin, Y. Pan, H. Ren, and J. Chen. 2008 · 2008
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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014 · 2014
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Backward and forward language modeling for constrained sentence generation
Lili Mou, Rui Yan, Ge Li, Lu Zhang, and Zhi Jin. 2015 · 2015
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Oriol Vinyals and Quoc Le. 2015 · 2015
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A diversity-promoting objective function for neural conversation models
Jiwei Li, Michel Galley, Chris Brockett, Jianfeng Gao, and Bill Dolan. 2016 · 2016
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How not to evaluate your dialogue system: An empirical study of unsupervised evaluation metrics for dialogue response generation
Chia-Wei Liu, Ryan Lowe, Iulian Serban, Mike Noseworthy, Laurent Charlin, and Joelle Pineau. 2016 · 2016
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Sequence to backward and forward sequences: A content-introducing approach to generative short-text conversation
Lili Mou, Yiping Song, Rui Yan, Ge Li, Lu Zhang, and Zhi Jin. 2016 · 2016
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Building end-to-end dialogue systems using generative hierarchical neural network models
Iulian Vlad Serban, Alessandro Sordoni, Yoshua Bengio, Aaron C Courville, and Joelle Pineau. 2016 · 2016
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Affect-lm: A neural language model for customizable affective text generation
Sayan Ghosh, Mathieu Chollet, Eugene Laksana, Louis-Philippe Morency, and Stefan Scherer. 2017 · 2017
A hierarchical latent variable encoder-decoder model for generating dialogues
Iulian Vlad Serban, Alessandro Sordoni, Ryan Lowe, Laurent Charlin, Joelle Pineau, Aaron Courville, and Yoshua Bengio. 2017 · 2017
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Personalized multitask learning for predicting tomorrow’s mood, stress, and health
Sara Ann Taylor, Natasha Jaques, Ehimwenma Nosakhare, Akane Sano, and Rosalind Picard. 2017 · 2017
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Topic aware neural response generation
Chen Xing, Wei Wu, Yu Wu, Jie Liu, Yalou Huang, Ming Zhou, and Wei-Ying Ma. 2017 · 2017
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Emotional chatting machine: emotional conversation generation with internal and external memory
Hao Zhou, Minlie Huang, Tianyang Zhang, Xiaoyan Zhu, and Bing Liu. 2017 · 2017
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Detecting anomalous emotion through big data from social networks based on a deep learning method
Xiao Sun, Chen Zhang, Shuai Ding, and Changqin Quan. 2018 · 2018
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Multimodal autoencoder: A deep learning approach to filling in missing sensor data and enabling better mood prediction
Natasha Jaques, Sara Taylor, Akane Sano, and Rosalind Picard. 2017 · 2017
Cited alongside, same era.