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Topic drift is a common phenomenon in multi-turn dialogue.
Measuring nominal scale agreement among many raters
Joseph L Fleiss · 1971
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Sequence to sequence learning with neural networks
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Neural machine translation by jointly learning to align and translate
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The ubuntu dialogue corpus: A large dataset for research in unstructured multi-turn dialogue systems
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A diversity-promoting objective function for neural conversation models
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Deep reinforcement learning for dialogue generation
Jiwei Li, Will Monroe, Alan Ritter, and Galley et al · 2016
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Building end-to-end dialogue systems using generative hierarchical neural network models
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Multi-view response selection for human-computer conversation
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Adversarial learning for neural dialogue generation
Jiwei Li, Will Monroe, Tianlin Shi, Alan Ritter, and Dan Jurafsky · 2017
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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 · 2017
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Multiresolution recurrent neural networks: An application to dialogue response generation
Iulian Vlad Serban, Tim Klinger, Gerald Tesauro, Kartik Talamadupula, Bowen Zhou, Yoshua Bengio, and Aaron Courville · 2017
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Topic aware neural response generation
Chen Xing, Wei Wu, Yu Wu, Jie Liu, Yalou Huang, Ming Zhou, and Wei-Ying Ma · 2017
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Hierarchical variational memory network for dialogue generation
Hongshen Chen, Zhaochun Ren, Jiliang Tang, Yihong Eric Zhao, and Dawei Yin · 2018
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Hierarchical recurrent attention network for response generation
Chen Xing, Yu Wu, Wei Wu, Yalou Huang, and Ming Zhou · 2018
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Reinforcing coherence for sequence to sequence model in dialogue generation
Hainan Zhang, Yanyan Lan, Jiafeng Guo, Jun Xu, and Xueqi Cheng · 2018
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Tailored sequence to sequence models to different conversation scenarios
Hainan Zhang, Yanyan Lan, Jiafeng Guo, Jun Xu, and Xueqi Cheng · 2018
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Context-sensitive generation of open-domain conversational responses
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A hierarchical latent variable encoder-decoder model for generating dialogues
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How to make context more useful? an empirical study on context-aware neural conversational models
Zhiliang Tian, Rui Yan, Lili Mou, Yiping Song, Yansong Feng, and Dongyan Zhao · 2017
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Sequential matching network: A new architecture for multi-turn response selection in retrieval-based chatbots
Yu Wu, Wei Wu, Chen Xing, Ming Zhou, and Zhoujun Li · 2017
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Weinan Zhang, Yiming Cui, Yifa Wang, Qingfu Zhu, Lingzhi Li, Lianqiang Zhou, and Ting Liu · 2018
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Recosa: Detecting the relevant contexts with self-attention for multi-turn dialogue generation
Hainan Zhang, Yanyan Lan, Liang Pang, Jiafeng Guo, and Xueqi Cheng · 2019
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