Fetching the paper…
Reading the bibliography…
While neural conversation models have shown great potentials towards generating informative and engaging responses via introducing external knowledge, learning such a model often requires knowledge-grounded dialogues that are difficult to obtain.
Measuring nominal scale agreement among many raters
J. L. Fleiss · 1971
Earlier work this paper cites.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
R. J. Williams · 1992
Earlier work this paper cites.
Okapi at trec-3
S. E. Robertson, S. Walker, S. Jones, M. M. Hancock-Beaulieu, M. Gatford, et al · 1995
Earlier work this paper cites.
Bleu: a method for automatic evaluation of machine translation
K. Papineni, S. Roukos, T. Ward, and W.-J. Zhu · 2002
Earlier work this paper cites.
Pattern recognition and machine learning
C. M. Bishop · 2006
Earlier work this paper cites.
Auto-encoding variational bayes
D. P. Kingma and M. Welling · 2013
Earlier work this paper cites.
Sequence to sequence learning with neural networks
I. Sutskever, O. Vinyals, and Q. V. Le · 2014
Earlier work this paper cites.
Generating sentences from a continuous space
S. R. Bowman, L. Vilnis, O. Vinyals, A. M. Dai, R. Jozefowicz, and S. Bengio · 2015
Earlier work this paper cites.
A diversity-promoting objective function for neural conversation models
J. Li, M. Galley, C. Brockett, J. Gao, and B. Dolan · 2015
Earlier work this paper cites.
Neural responding machine for short-text conversation
L. Shang, Z. Lu, and H. Li · 2015
Earlier work this paper cites.
O. Vinyals and Q. Le · 2015
Earlier work this paper cites.
Infogan: Interpretable representation learning by information maximizing generative adversarial nets
X. Chen, Y. Duan, R. Houthooft, J. Schulman, I. Sutskever, and P. Abbeel · 2016
Earlier work this paper cites.
Zero-resource translation with multi-lingual neural machine translation
O. Firat, B. Sankaran, Y. Al-Onaizan, F. T. Y. Vural, and K. Cho · 2016
Earlier work this paper cites.
Categorical reparameterization with gumbel-softmax
E. Jang, S. Gu, and B. Poole · 2016
Earlier work this paper cites.
A persona-based neural conversation model
J. Li, M. Galley, C. Brockett, G. Spithourakis, J. Gao, and B. Dolan · 2016
Earlier work this paper cites.
Building end-to-end dialogue systems using generative hierarchical neural network models
I. V. Serban, A. Sordoni, Y. Bengio, A. C. Courville, and J. Pineau · 2016
Earlier work this paper cites.
End-to-end dialogue systems using generative hierarchical neural network models
I. V. Serban, A. Sordoni, Y. Bengio, A. C. Courville, and J. Pineau · 2016
Earlier work this paper cites.
Unsupervised neural machine translation
M. Artetxe, G. Labaka, E. Agirre, and K. Cho · 2017
Earlier work this paper cites.
A teacher-student framework for zero-resource neural machine translation
Y. Chen, Y. Liu, Y. Cheng, and V. O. Li · 2017
Cited alongside, same era.
Unsupervised machine translation using monolingual corpora only
G. Lample, A. Conneau, L. Denoyer, and M. Ranzato · 2017
Cited alongside, same era.
A hierarchical latent variable encoder-decoder model for generating dialogues
I. V. Serban, A. Sordoni, R. Lowe, L. Charlin, J. Pineau, A. C. Courville, and Y. Bengio · 2017
Cited alongside, same era.
Attention is all you need
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin · 2017
Cited alongside, same era.
Topic aware neural response generation
C. Xing, W. Wu, J. Liu, Y. Huang, M. Zhou, and W.-Y. Ma · 2017
Cited alongside, same era.
Personalizing dialogue agents: I have a dog, do you have pets too?
S. Zhang, E. Dinan, J. Urbanek, A. Szlam, D. Kiela, and J. Weston · 2018
Later among the works it cites.
Commonsense knowledge aware conversation generation with graph attention
H. Zhou, T. Young, M. Huang, H. Zhao, J. Xu, and X. Zhu · 2018
Later among the works it cites.
A dataset for document grounded conversations
K. Zhou, S. Prabhumoye, and A. W. Black · 2018
Later among the works it cites.
Wizard of wikipedia: Knowledge-powered conversational agents
E. Dinan, S. Roller, K. Shuster, A. Fan, M. Auli, and J. Weston · 2019
Later among the works it cites.
Unified language model pre-training for natural language understanding and generation
L. Dong, N. Yang, W. Wang, F. Wei, X. Liu, Y. Wang, J. Gao, M. Zhou, and H.-W. Hon · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
T. Zhao, R. Zhao, and M. Eskenazi · 2017
Cited alongside, same era.
Emotional chatting machine: Emotional conversation generation with internal and external memory
H. Zhou, M. Huang, T. Zhang, X. Zhu, and B. Liu · 2017
Cited alongside, same era.
Meansum: a neural model for unsupervised multi-document abstractive summarization
E. Chu and P. J. Liu · 2018
Cited alongside, same era.
Differentiable perturb-and-parse: Semi-supervised parsing with a structured variational autoencoder
C. Corro and I. Titov · 2018
Cited alongside, same era.
Augmenting neural response generation with context-aware topical attention
N. Dziri, E. Kamalloo, K. W. Mathewson, and O. Zaiane · 2018
Cited alongside, same era.
A knowledge-grounded neural conversation model
M. Ghazvininejad, C. Brockett, M.-W. Chang, B. Dolan, J. Gao, W.-t. Yih, and M. Galley · 2018
Cited alongside, same era.
Phrase-based & neural unsupervised machine translation
G. Lample, M. Ott, A. Conneau, L. Denoyer, and M. Ranzato · 2018
Cited alongside, same era.
Topical-chat: Towards knowledge-grounded open-domain conversations
K. Gopalakrishnan, B. Hedayatnia, Q. Chen, A. Gottardi, S. Kwatra, A. Venkatesh, R. Gabriel, D. Hakkani-Tür, and A. A. AI · 2019
Later among the works it cites.
Incremental transformer with deliberation decoder for document grounded conversations
Z. Li, C. Niu, F. Meng, Y. Feng, Q. Li, and J. Zhou · 2019
Later among the works it cites.
Learning to select knowledge for response generation in dialog systems
R. Lian, M. Xie, F. Wang, J. Peng, and H. Wu · 2019
Later among the works it cites.
Opendialkg: Explainable conversational reasoning with attention-based walks over knowledge graphs
S. Moon, P. Shah, A. Kumar, and R. Subba · 2019
Later among the works it cites.
Language models are unsupervised multitask learners
A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, and I. Sutskever · 2019
Later among the works it cites.
Unsupervised neural machine translation with smt as posterior regularization
S. Ren, Z. Zhang, S. Liu, M. Zhou, and S. Ma · 2019
Later among the works it cites.
What makes a good conversation? how controllable attributes affect human judgments
A. See, S. Roller, D. Kiela, and J. Weston · 2019
Later among the works it cites.
Dykgchat: Benchmarking dialogue generation grounding on dynamic knowledge graphs
Y.-L. Tuan, Y.-N. Chen, and H.-y. Lee · 2019
Later among the works it cites.
Neural response generation with meta-words
C. Xu, W. Wu, C. Tao, H. Hu, M. Schuerman, and Y. Wang · 2019
Later among the works it cites.
Dialogpt: Large-scale generative pre-training for conversational response generation
Y. Zhang, S. Sun, M. Galley, Y.-C. Chen, C. Brockett, X. Gao, J. Gao, J. Liu, and B. Dolan · 2019
Later among the works it cites.
Towards a human-like open-domain chatbot
D. Adiwardana, M.-T. Luong, D. R. So, J. Hall, N. Fiedel, R. Thoppilan, Z. Yang, A. Kulshreshtha, G. Nemade, Y. Lu, et al · 2020
Closest in time.
Sequential latent knowledge selection for knowledge-grounded dialogue
B. Kim, J. Ahn, and G. Kim · 2020
Closest in time.
Low-resource knowledge-grounded dialogue generation
X. Zhao, W. Wu, C. Tao, C. Xu, D. Zhao, and R. Yan · 2020
Closest in time.