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Most of the existing works for dialogue generation are data-driven models trained directly on corpora crawled from websites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
Earlier work this paper cites.
Dataset filtering techniques in constraint-based frequent pattern mining
Marek Wojciechowski and Maciej Zakrzewicz · 2002
Earlier work this paper cites.
TextRank: Bringing order into text
Rada Mihalcea and Paul Tarau · 2004
Earlier work this paper cites.
Dimensionality reduction by learning an invariant mapping
Raia Hadsell, Sumit Chopra, and Yann LeCun · 2006
Earlier work this paper cites.
Instance weighting for domain adaptation in NLP
Jing Jiang and ChengXiang Zhai · 2007
Earlier work this paper cites.
Data-driven response generation in social media
Alan Ritter, Colin Cherry, and William B. Dolan · 2011
Earlier work this paper cites.
Learning phrase representations using RNN encoder–decoder for statistical machine translation
Kyunghyun Cho, Bart van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio · 2014
Earlier work this paper cites.
Convolutional neural networks for sentence classification
Yoon Kim · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V Le · 2014
Earlier work this paper cites.
Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio · 2015
Earlier work this paper cites.
Neural responding machine for short-text conversation
Lifeng Shang, Zhengdong Lu, and Hang Li · 2015
Earlier work this paper cites.
Oriol Vinyals and Quoc Le · 2015
Earlier work this paper cites.
A diversity-promoting objective function for neural conversation models
Jiwei Li, Michel Galley, Chris Brockett, Jianfeng Gao, and Bill Dolan · 2016
Earlier work this paper cites.
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
Earlier work this paper cites.
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
Cited alongside, same era.
Building end-to-end dialogue systems using generative hierarchical neural network models
Iulian V Serban, Alessandro Sordoni, Yoshua Bengio, Aaron Courville, and Joelle Pineau · 2016
Cited alongside, same era.
Adversarial learning for neural dialogue generation
Jiwei Li, Will Monroe, Tianlin Shi, Sébastien Jean, Alan Ritter, and Dan Jurafsky · 2017
Cited alongside, same era.
Adversarial ranking for language generation
Kevin Lin, Dianqi Li, Xiaodong He, Zhengyou Zhang, and Ming-Ting Sun · 2017
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
Cited alongside, same era.
Chat more: Deepening and widening the chatting topic via a deep model
Wenjie Wang, Minlie Huang, Xin-Shun Xu, Fumin Shen, and Liqiang Nie · 2018
Later among the works it cites.
Better conversations by modeling, filtering, and optimizing for coherence and diversity
Xinnuo Xu, Ondřej Dušek, Ioannis Konstas, and Verena Rieser · 2018
Later among the works it cites.
Elastic responding machine for dialog generation with dynamically mechanism selecting
Ganbin Zhou, Ping Luo, Yijun Xiao, Fen Lin, Bo Chen, and Qing He · 2018
Later among the works it cites.
Skeleton-to-response: Dialogue generation guided by retrieval memory
Deng Cai, Yan Wang, Wei Bi, Zhaopeng Tu, Xiaojiang Liu, Wai Lam, and Shuming Shi · 2019
Later among the works it cites.
Improving neural conversational models with entropy-based data filtering
Richárd Csáky, Patrik Purgai, and Gábor Recski · 2019
Later among the works it cites.
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Chen Xing, Wei Wu, Yu Wu, Jie Liu, Yalou Huang, Ming Zhou, and Wei-Ying Ma · 2017
Cited alongside, same era.
Neural response generation via GAN with an approximate embedding layer
Zhen Xu, Bingquan Liu, Baoxun Wang, Chengjie Sun, Xiaolong Wang, Zhuoran Wang, and Chao Qi · 2017
Cited alongside, same era.
Seqgan: Sequence generative adversarial nets with policy gradient
Lantao Yu, Weinan Zhang, Jun Wang, and Yong Yu · 2017
Cited alongside, same era.
Mechanism-aware neural machine for dialogue response generation
Ganbin Zhou, Ping Luo, Rongyu Cao, Fen Lin, Bo Chen, and Qing He · 2017
Cited alongside, same era.
Variational autoregressive decoder for neural response generation
Jiachen Du, Wenjie Li, Yulan He, Ruifeng Xu, Lidong Bing, and Xuan Wang · 2018
Cited alongside, same era.
Towards less generic responses in neural conversation models: A statistical re-weighting method
Yahui Liu, Wei Bi, Jun Gao, Xiaojiang Liu, Jian Yao, and Shuming Shi · 2018
Cited alongside, same era.
S2SPMN: A simple and effective framework for response generation with relevant information
Jiaxin Pei and Chenliang Li · 2018
Cited alongside, same era.
Generating multiple diverse responses for short-text conversation
Jun Gao, Wei Bi, Xiaojiang Liu, Junhui Li, and Shuming Shi · 2019
Later among the works it cites.
A discrete CVAE for response generation on short-text conversation
Jun Gao, Wei Bi, Xiaojiang Liu, Junhui Li, Guodong Zhou, and Shuming Shi · 2019
Later among the works it cites.
Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2019
Later among the works it cites.
Data manipulation: Towards effective instance learning for neural dialogue generation via learning to augment and reweight
Hengyi Cai, Hongshen Chen, Yonghao Song, Cheng Zhang, Xiaofang Zhao, and Dawei Yin · 2020
Closest in time.
A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
Closest in time.
Negative training for neural dialogue response generation
Tianxing He and James Glass · 2020
Closest in time.
Pretraining with contrastive sentence objectives improves discourse performance of language models
Dan Iter, Kelvin Guu, Larry Lansing, and Dan Jurafsky · 2020
Closest in time.
Response-anticipated memory for on-demand knowledge integration in response generation
Zhiliang Tian, Wei Bi, Dongkyu Lee, Lanqing Xue, Yiping Song, Xiaojiang Liu, and Nevin L. Zhang · 2020
Closest in time.
DIALOGPT : Large-scale generative pre-training for conversational response generation
Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, and Bill Dolan · 2020
Closest in time.