Fetching the paper…
Reading the bibliography…
Recent advances in open-domain dialogue systems rely on the success of neural models that are trained on large-scale data.
Personalized dialogue generation with diversified traits
Yinhe Zheng, Guanyi Chen, Minlie Huang, Song Liu, and Xuan Zhu. 2019 · 1901
Earlier work this paper cites.
Convert: Efficient and accurate conversational representations from transformers
Matthew Henderson, Iñigo Casanueva, Nikola Mrkšić, Pei-Hao Su, Tsung-Hsien Wen, and Ivan Vulić. 2020 · 1911
Earlier work this paper cites.
Statistical significance tests for machine translation evaluation
Philipp Koehn. 2004 · 2004
Earlier work this paper cites.
Free-marginal multirater kappa (multirater k [free]): An alternative to fleiss’ fixed-marginal multirater kappa
Justus J Randolph. 2005 · 2005
Earlier work this paper cites.
Diversifying dialogue generation with non-conversational text
Hui Su, Xiaoyu Shen, Sanqiang Zhao, Xiao Zhou, Pengwei Hu, Randy Zhong, Cheng Niu, and Jie Zhou. 2020 · 2005
Earlier work this paper cites.
A deep architecture for matching short texts
Zhengdong Lu and Hang Li. 2013 · 2013
Earlier work this paper cites.
Sequence to sequence learning with neural networks
I Sutskever, O Vinyals, and QV Le. 2014 · 2014
Earlier work this paper cites.
Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. 2015 · 2015
Earlier work this paper cites.
A neural conversational model
Oriol Vinyals and Quoc Le. 2015 · 2015
Earlier work this paper cites.
Sequence-level knowledge distillation
Yoon Kim and Alexander M. Rush. 2016 · 2016
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 · 2016
Earlier work this paper cites.
Improving neural machine translation models with monolingual data
Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016 · 2016
Earlier work this paper cites.
Efficient natural language response suggestion for smart reply
Matthew Henderson, Rami Al-Rfou, Brian Strope, Yun-hsuan Sung, Laszlo Lukacs, Ruiqi Guo, Sanjiv Kumar, Balint Miklos, and Ray Kurzweil. 2017 · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Cited alongside, same era.
Generative encoder-decoder models for task-oriented spoken dialog systems with chatting capability
Tiancheng Zhao, Allen Lu, Kyusong Lee, and Maxine Eskenazi. 2017 · 2017
Cited alongside, same era.
Data augmentation for neural online chats response selection
Wenchao Du and Alan Black. 2018 · 2018
Cited alongside, same era.
A knowledge-grounded neural conversation model
Marjan Ghazvininejad, Chris Brockett, Ming-Wei Chang, Bill Dolan, Jianfeng Gao, Wen-tau Yih, and Michel Galley. 2018 · 2018
Cited alongside, same era.
Assigning personality/profile to a chatting machine for coherent conversation generation
Qiao Qian, Minlie Huang, Haizhou Zhao, Jingfang Xu, and Xiaoyan Zhu. 2018 · 2018
Cited alongside, same era.
Improving language understanding by generative pre-training
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Later among the works it cites.
Large-scale transfer learning for natural language generation
Sergey Golovanov, Rauf Kurbanov, Sergey Nikolenko, Kyryl Truskovskyi, Alexander Tselousov, and Thomas Wolf. 2019 · 2019
Later among the works it cites.
Insufficient data can also rock! learning to converse using smaller data with augmentation
Juntao Li, Lisong Qiu, Bo Tang, Dongmin Chen, Dongyan Zhao, and Rui Yan. 2019 · 2019
Later among the works it cites.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
Later among the works it cites.
Eda: Easy data augmentation techniques for boosting performance on text classification tasks
Jason W Wei and Kai Zou. 2019 · 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…
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. 2018 · 2018
Cited alongside, same era.
From eliza to xiaoice: challenges and opportunities with social chatbots
Heung-Yeung Shum, Xiao-dong He, and Di Li. 2018 · 2018
Cited alongside, same era.
Sentigan: Generating sentimental texts via mixture adversarial networks
Ke Wang and Xiaojun Wan. 2018 · 2018
Cited alongside, same era.
Qanet: Combining local convolution with global self-attention for reading comprehension
Adams Wei Yu, David Dohan, Minh-Thang Luong, Rui Zhao, Kai Chen, Mohammad Norouzi, and Quoc V Le. 2018 · 2018
Cited alongside, same era.
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
Cited alongside, same era.
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 · 2019
Cited alongside, same era.
Autoaugment: Learning augmentation policies from data
Ekin D Cubuk, Barret Zoph, Dandelion Mane, Vijay Vasudevan, and Quoc V Le. 2019 · 2019
Cited alongside, same era.
Yu Wu, Furu Wei, Shaohan Huang, Yunli Wang, Zhoujun Li, and Ming Zhou. 2019 · 2019
Later among the works it cites.
Retrieval-enhanced adversarial training for neural response generation
Qingfu Zhu, Lei Cui, Wei-Nan Zhang, Furu Wei, and Ting Liu. 2019 · 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 · 2020
Closest in time.
Challenges in building intelligent open-domain dialog systems
Minlie Huang, Xiaoyan Zhu, and Jianfeng Gao. 2020 · 2020
Closest in time.
A large-scale chinese short-text conversation dataset
Yida Wang, Pei Ke, Yinhe Zheng, Kaili Huang, Yong Jiang, Xiaoyan Zhu, and Minlie Huang. 2020 · 2020
Closest in time.
An effective domain adaptive post-training method for bert in response selection
Taesun Whang, Dongyub Lee, Chanhee Lee, Kisu Yang, Dongsuk Oh, and HeuiSeok Lim. 2020 · 2020
Closest in time.
KdConv: A Chinese multi-domain dialogue dataset towards multi-turn knowledge-driven conversation
Hao Zhou, Chujie Zheng, Kaili Huang, Minlie Huang, and Xiaoyan Zhu. 2020 · 2020
Closest in time.