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Background Based Conversations (BBCs) have been developed to make dialogue systems generate more informative and natural responses by leveraging background knowledge.
On the properties of neural machine translation: Encoder-decoder approaches
Kyunghyun Cho, Bart Van Merrienboer, Dzmitry Bahdanau, and Yoshua Bengio · 2014
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Empirical evaluation of gated recurrent neural networks on sequence modeling
Junyoung Chung, Caglar Gulcehre, Kyunghyun Cho, and Yoshua Bengio · 2014
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Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V Le · 2014
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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio · 2015
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Pointer networks
Oriol Vinyals, Meire Fortunato, and Navdeep Jaitly · 2015
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Learning recurrent span representations for extractive question answering
Kenton Lee, Shimi Salant, Tom Kwiatkowski, Ankur Parikh, Dipanjan Das, and Jonathan Berant · 2016
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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
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Squad: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang · 2016
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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.
Get to the point: Summarization with pointer-generator networks
Abigail See, Peter J Liu, and Christopher D Manning · 2017
Cited alongside, same era.
Bidirectional attention flow for machine comprehension
Minjoon Seo, Aniruddha Kembhavi, Ali Farhadi, and Hannaneh Hajishirzi · 2017
Cited alongside, same era.
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
Cited alongside, same era.
Machine comprehension using match-lstm and answer pointer
Shuohang Wang and Jing Jiang · 2017
Cited alongside, same era.
Gated self-matching networks for reading comprehension and question answering
Towards exploiting background knowledge for building conversation systems
Nikita Moghe, Siddhartha Arora, Suman Banerjee, and Mitesh M Khapra · 2018
Later among the works it cites.
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
Later among the works it cites.
Generating informative and diverse conversational responses via adversarial information maximization
Yizhe Zhang, Michel Galley, Jianfeng Gao, Zhe Gan, Xiujun Li, Chris Brockett, and Bill Dolan · 2018
Later among the works it cites.
Commonsense knowledge aware conversation generation with graph attention
Hao Zhou, Tom Young, Minlie Huang, Haizhou Zhao, Jingfang Xu, and Xiaoyan Zhu · 2018
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Wizard of wikipedia: Knowledge-powered conversational agents
Emily Dinan, Stephen Roller, Kurt Shuster, Angela Fan, Michael Auli, and Jason Weston · 2019
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Wenhui Wang, Nan Yang, Furu Wei, Baobao Chang, and Ming Zhou · 2017
Cited alongside, same era.
End-to-end answer chunk extraction and ranking for reading comprehension
Yang Yu, Wei Zhang, Kazi Hasan, Mo Yu, Bing Xiang, and Bowen Zhou · 2017
Cited alongside, same era.
Knowledge diffusion for neural dialogue generation
Shuman Liu, Hongshen Chen, Zhaochun Ren, Yang Feng, Qun Liu, and Dawei Yin · 2018
Cited alongside, same era.
Improving neural response diversity with frequency-aware cross-entropy loss
Shaojie Jiang, Pengjie Ren, Christof Monz, and Maarten de Rijke · 2019
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Learning to select knowledge for response generation in dialog systems
Rongzhong Lian, Min Xie, Fan Wang, Jinhua Peng, and Hua Wu · 2019
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