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Current state-of-the-art neural dialogue models learn from human conversations following the data-driven paradigm.
Emotional chatting machine: Emotional conversation generation with internal and external memory
Hao Zhou, Minlie Huang, Tianyang Zhang, Xiaoyan Zhu, and Bing Liu. 2018 · 2004
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Instance weighting for domain adaptation in NLP
Jing Jiang and ChengXiang Zhai. 2007 · 2007
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Class noise mitigation through instance weighting
Umaa Rebbapragada and Carla E. Brodley. 2007 · 2007
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Auto-encoding variational bayes
Diederik P. Kingma and Max Welling. 2014 · 2014
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Neural machine translation by jointly learning to align and translate
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Adam: A method for stochastic optimization
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Labeled data generation with encoder-decoder LSTM for semantic slot filling
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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 · 2016
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Opensubtitles2016: Extracting large parallel corpora from movie and TV subtitles
Pierre Lison and Jörg Tiedemann. 2016 · 2016
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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, Michael Noseworthy, Laurent Charlin, and Joelle Pineau. 2016 · 2016
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Improving neural machine translation models with monolingual data
Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016 · 2016
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A simple but tough-to-beat baseline for sentence embeddings
Sanjeev Arora, Yingyu Liang, and Tengyu Ma. 2017 · 2017
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Categorical reparameterization with gumbel-softmax
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Dailydialog: A manually labelled multi-turn dialogue dataset
Yanran Li, Hui Su, Xiaoyu Shen, Wenjie Li, Ziqiang Cao, and Shuzi Niu. 2017 · 2017
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Not all dialogues are created equal: Instance weighting for neural conversational models
Pierre Lison and Serge Bibauw. 2017 · 2017
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A hierarchical latent variable encoder-decoder model for generating dialogues
Iulian Vlad Serban, Alessandro Sordoni, Ryan Lowe, Laurent Charlin, Joelle Pineau, Aaron C. Courville, and Yoshua Bengio. 2017 · 2017
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A conditional variational framework for dialog generation
Xiaoyu Shen, Hui Su, Yanran Li, Wenjie Li, Shuzi Niu, Yang Zhao, Akiko Aizawa, and Guoping Long. 2017 · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Learning to converse with noisy data: Generation with calibration
Mingyue Shang, Zhenxin Fu, Nanyun Peng, Yansong Feng, Dongyan Zhao, and Rui Yan. 2018 · 2018
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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
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Improving neural conversational models with entropy-based data filtering
Richárd Csáky, Patrik Purgai, and Gábor Recski. 2019 · 2019
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BERT: pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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Dialogwae: Multimodal response generation with conditional wasserstein auto-encoder
Xiaodong Gu, Kyunghyun Cho, JungWoo Ha, and Sunghun Kim. 2019 · 2019
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Rui Wang, Masao Utiyama, Lemao Liu, Kehai Chen, and Eiichiro Sumita. 2017 · 2017
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Data noising as smoothing in neural network language models
Ziang Xie, Sida I. Wang, Jiwei Li, Daniel Lévy, Aiming Nie, Dan Jurafsky, and Andrew Y. Ng. 2017 · 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 · 2017
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Learning discourse-level diversity for neural dialog models using conditional variational autoencoders
Tiancheng Zhao, Ran Zhao, and Maxine Eskénazi. 2017 · 2017
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Understanding back-translation at scale
Sergey Edunov, Myle Ott, Michael Auli, and David Grangier. 2018 · 2018
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Sequence-to-sequence data augmentation for dialogue language understanding
Yutai Hou, Yijia Liu, Wanxiang Che, and Ting Liu. 2018 · 2018
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Contextual augmentation: Data augmentation by words with paradigmatic relations
Sosuke Kobayashi. 2018 · 2018
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Learning data manipulation for augmentation and weighting
Zhiting Hu, Bowen Tan, Ruslan Salakhutdinov, Tom M. Mitchell, and Eric P. Xing. 2019 · 2019
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Insufficient data can also rock! learning to converse using smaller data with augmentation
Juntao Li, Lisong Qiu, Bo Tang, Min Dong Chen, Dongyan Zhao, and Rui Yan. 2019 · 2019
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Facebook fair’s wmt19 news translation task submission
Nathan Ng, Kyra Yee, Alexei Baevski, Myle Ott, Michael Auli, and Sergey Edunov. 2019 · 2019
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Automatically learning data augmentation policies for dialogue tasks
Tong Niu and Mohit Bansal. 2019 · 2019
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Exploiting persona information for diverse generation of conversational responses
Haoyu Song, Weinan Zhang, Yiming Cui, Dong Wang, and Ting Liu. 2019 · 2019
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Connecting the dots between MLE and RL for sequence generation
Bowen Tan, Zhiting Hu, Zichao Yang, Ruslan Salakhutdinov, and Eric P. Xing. 2019 · 2019
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Conditional BERT contextual augmentation
Xing Wu, Shangwen Lv, Liangjun Zang, Jizhong Han, and Songlin Hu. 2019 · 2019
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Good-enough compositional data augmentation
Jacob Andreas. 2020 · 2020
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Learning from easy to complex: Adaptive multi-curricula learning for neural dialogue generation
Hengyi Cai, Hongshen Chen, Cheng Zhang, Yonghao Song, Xiaofang Zhao, Yangxi Li, Dongsheng Duan, and Dawei Yin. 2020 · 2020
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