Fetching the paper…
Reading the bibliography…
Neural conversation models tend to generate safe, generic responses for most inputs.
Eliza—a computer program for the study of natural language communication between man and machine
Joseph Weizenbaum. 1966 · 1966
Earlier work this paper cites.
An introduction to the bootstrap
Bradley Efron and Robert J Tibshirani. 1994 · 1994
Earlier work this paper cites.
A neural probabilistic language model
Yoshua Bengio, Réjean Ducharme, Pascal Vincent, and Christian Jauvin. 2003 · 2003
Earlier work this paper cites.
Evaluating content selection in summarization: The pyramid method
Ani Nenkova and Rebecca Passonneau. 2004 · 2004
Earlier work this paper cites.
Collective content selection for concept-to-text generation
Regina Barzilay and Mirella Lapata. 2005 · 2005
Earlier work this paper cites.
Integrating topics and syntax
Thomas L Griffiths, Mark Steyvers, David M Blei, and Joshua B Tenenbaum. 2005 · 2005
Earlier work this paper cites.
Generalized expectation criteria for semi-supervised learning of conditional random fields
Gideon S Mann and Andrew McCallum. 2008 · 2008
Earlier work this paper cites.
News from opus-a collection of multilingual parallel corpora with tools and interfaces
Jörg Tiedemann. 2009 · 2009
Earlier work this paper cites.
Unsupervised modeling of twitter conversations
Alan Ritter, Colin Cherry, and Bill Dolan. 2010 · 2010
Earlier work this paper cites.
Chameleons in imagined conversations: A new approach to understanding coordination of linguistic style in dialogs
Cristian Danescu-Niculescu-Mizil and Lillian Lee. 2011 · 2011
Earlier work this paper cites.
Data-driven response generation in social media
Alan Ritter, Colin Cherry, and William B Dolan. 2011 · 2011
Earlier work this paper cites.
Generating text with recurrent neural networks
Ilya Sutskever, James Martens, and Geoffrey Hinton. 2011 · 2011
Earlier work this paper cites.
An empirical investigation of statistical significance in nlp
Taylor Berg-Kirkpatrick, David Burkett, and Dan Klein. 2012 · 2012
Earlier work this paper cites.
A systematic exploration of diversity in machine translation
Kevin Gimpel, Dhruv Batra, Chris Dyer, and Gregory Shakhnarovich. 2013 · 2013
Earlier work this paper cites.
The dialog state tracking challenge
Jason Williams, Antoine Raux, Deepak Ramachandran, and Alan Black. 2013 · 2013
Earlier work this paper cites.
Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014 · 2014
Cited alongside, same era.
Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V Le. 2014 · 2014
Cited alongside, same era.
Bayesian inference with posterior regularization and applications to infinite latent svms
Jun Zhu, Ning Chen, and Eric P Xing. 2014 · 2014
Cited alongside, same era.
Evaluating prerequisite qualities for learning end-to-end dialog systems
Jesse Dodge, Andreea Gane, Xiang Zhang, Antoine Bordes, Sumit Chopra, Alexander Miller, Arthur Szlam, and Jason Weston. 2015 · 2015
Cited alongside, same era.
Skip-thought vectors
Ryan Kiros, Yukun Zhu, Ruslan R Salakhutdinov, Richard Zemel, Raquel Urtasun, Antonio Torralba, and Sanja Fidler. 2015 · 2015
Cited alongside, same era.
Diverse beam search: Decoding diverse solutions from neural sequence models
Ashwin K Vijayakumar, Michael Cogswell, Ramprasath R Selvaraju, Qing Sun, Stefan Lee, David Crandall, and Dhruv Batra. 2016 · 2016
Later among the works it cites.
Opennmt: Open-source toolkit for neural machine translation
Guillaume Klein, Yoon Kim, Yuntian Deng, Jean Senellart, and Alexander M. Rush. 2017 · 2017
Later among the works it cites.
Deal or no deal? end-to-end learning of negotiation dialogues
Mike Lewis, Denis Yarats, Yann Dauphin, Devi Parikh, and Dhruv Batra. 2017 · 2017
Later among the works it cites.
Adversarial learning for neural dialogue generation
Jiwei Li, Will Monroe, Tianlin Shi, Sėbastien Jean, Alan Ritter, and Dan Jurafsky. 2017 · 2017
Later among the works it cites.
Towards an automatic turing test: Learning to evaluate dialogue responses
Ryan Lowe, Michael Noseworthy, Iulian Vlad Serban, Nicolas Angelard-Gontier, Yoshua Bengio, and Joelle Pineau. 2017 · 2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Neural responding machine for short-text conversation
Lifeng Shang, Zhengdong Lu, and Hang Li. 2015 · 2015
Cited alongside, same era.
A neural network approach to context-sensitive generation of conversational responses
Alessandro Sordoni, Michel Galley, Michael Auli, Chris Brockett, Yangfeng Ji, Margaret Mitchell, Jian-Yun Nie, Jianfeng Gao, and Bill Dolan. 2015 · 2015
Cited alongside, same era.
Semantically conditioned lstm-based natural language generation for spoken dialogue systems
Tsung-Hsien Wen, Milica Gasic, Nikola Mrkšić, Pei-Hao Su, David Vandyke, and Steve Young. 2015 · 2015
Cited alongside, same era.
A simple but tough-to-beat baseline for sentence embeddings
Sanjeev Arora, Yingyu Liang, and Tengyu Ma. 2016 · 2016
Cited alongside, same era.
Smart reply: Automated response suggestion for email
Anjuli Kannan, Karol Kurach, Sujith Ravi, Tobias Kaufmann, Andrew Tomkins, Balint Miklos, Greg Corrado, László Lukács, Marina Ganea, Peter Young, et al. 2016 · 2016
Cited alongside, same era.
A diversity-promoting objective function for neural conversation models
Jiwei Li, Michel Galley, Chris Brockett, Jianfeng Gao, and Bill Dolan. 2016a · 2016
Cited alongside, same era.
Mutual information and diverse decoding improve neural machine translation
Jiwei Li and Dan Jurafsky. 2016 · 2016
Cited alongside, same era.
Generating high-quality and informative conversation responses with sequence-to-sequence models
Yuanlong Shao, Stephan Gouws, Denny Britz, Anna Goldie, Brian Strope, and Ray Kurzweil. 2017 · 2017
Later among the works it cites.
Steering output style and topic in neural response generation
Di Wang, Nebojsa Jojic, Chris Brockett, and Eric Nyberg. 2017 · 2017
Later among the works it cites.
Topic aware neural response generation
Chen Xing, Wei Wu, Yu Wu, Jie Liu, Yalou Huang, Ming Zhou, and Wei-Ying Ma. 2017 · 2017
Later among the works it cites.
Learning conversational systems that interleave task and non-task content
Zhou Yu, Alexander Rudnicky, and Alan Black. 2017 · 2017
Later among the works it cites.
Discourse-aware neural rewards for coherent text generation
Antoine Bosselut, Asli Celikyilmaz, Xiaodong He, Jianfeng Gao, Po-Sen Huang, and Yejin Choi. 2018 · 2018
Closest in time.
Quac : Question answering in context
Eunsol Choi, He He, Mohit Iyyer, Mark Yatskar, Wen-tau Yih, Yejin Choi, Percy Liang, and Luke Zettlemoyer. 2018 · 2018
Closest in time.
Polite dialogue generation without parallel data
Tong Niu and Mohit Bansal. 2018 · 2018
Closest in time.
Personalizing dialogue agents: I have a dog, do you have pets too?
Saizheng Zhang, Emily Dinan, Jack Urbanek, Arthur Szlam, Douwe Kiela, and Jason Weston. 2018 · 2018
Closest in time.
Posterior regularization for structured latent variable models
Kuzman Ganchev, Jennifer Gillenwater, Ben Taskar, et al. 2010 · 2049
Closest in time.
Revisiting recurrent networks for paraphrastic sentence embeddings
John Wieting and Kevin Gimpel. 2017 · 2088
Closest in time.