Fetching the paper…
Reading the bibliography…
Pre-trained language models based on general text enable huge success in the NLP scenario.
Transfertransfo: A transfer learning approach for neural network based conversational agents
Thomas Wolf, Victor Sanh, Julien Chaumond, and Clement Delangue. 2019 · 1901
Earlier work this paper cites.
Training neural response selection for task-oriented dialogue systems
Matthew Henderson, Ivan Vulic, Daniel Gerz, Iñigo Casanueva, Paweł Budzianowski, Sam Coope, Georgios P. Spithourakis, Tsung-Hsien Wen, Nikola Mrksic, and Pei hao Su. 2019 · 1906
Earlier work this paper cites.
Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 1907
Earlier work this paper cites.
Convert: Efficient and accurate conversational representations from transformers
Matthew Henderson, Iñigo Casanueva, Nikola Mrkvsi’c, Pei hao Su, Tsung-Hsien, and Ivan Vulic. 2020 · 1911
Earlier work this paper cites.
Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey E. Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014 · 1958
Earlier work this paper cites.
Cert: Contrastive self-supervised learning for language understanding
Hongchao Fang, Sicheng Wang, Meng Zhou, Jiayuan Ding, and Pengtao Xie. 2020 · 2005
Earlier work this paper cites.
The second dialog state tracking challenge
Matthew Henderson, Blaise Thomson, and J. Williams. 2014 · 2014
Earlier work this paper cites.
Frames: a corpus for adding memory to goal-oriented dialogue systems
Layla El Asri, Hannes Schulz, Shikhar Sharma, Jeremie Zumer, Justin Harris, Emery Fine, Rahul Mehrotra, and Kaheer Suleman. 2017 · 2017
Earlier work this paper cites.
Key-value retrieval networks for task-oriented dialogue
Mihail Eric, Lakshmi. Krishnan, François Charette, and Christopher D. Manning. 2017 · 2017
Earlier work this paper cites.
Neural belief tracker: Data-driven dialogue state tracking
Nikola Mrksic, Diarmuid Ó Séaghdha, Tsung-Hsien Wen, Blaise Thomson, and Steve J. Young. 2017 · 2017
Earlier work this paper cites.
A network-based end-to-end trainable task-oriented dialogue system
Lina Maria Rojas-Barahona, Milica Gavsic, Nikola Mrksic, Pei hao Su, Stefan Ultes, Tsung-Hsien Wen, Steve J. Young, and David Vandyke. 2017 · 2017
Earlier work this paper cites.
Multiwoz - a large-scale multi-domain wizard-of-oz dataset for task-oriented dialogue modelling
Paweł Budzianowski, Tsung-Hsien Wen, Bo-Hsiang Tseng, Iñigo Casanueva, Stefan Ultes, Osman Ramadan, and Milica Gasic. 2018 · 2018
Earlier work this paper cites.
Microsoft dialogue challenge: Building end-to-end task-completion dialogue systems
Xiujun Li, Sarah Panda, Jingjing Liu, and Jianfeng Gao. 2018 · 2018
Earlier work this paper cites.
A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel R. Bowman. 2018 · 2018
Cited alongside, same era.
A theoretical analysis of contrastive unsupervised representation learning
Sanjeev Arora, Hrishikesh Khandeparkar, Mikhail Khodak, Orestis Plevrakis, and Nikunj Saunshi. 2019 · 2019
Cited alongside, same era.
Taskmaster-1: Toward a realistic and diverse dialog dataset
Bill Byrne, Karthik Krishnamoorthi, Chinnadhurai Sankar, Arvind Neelakantan, Daniel Duckworth, Semih Yavuz, Ben Goodrich, Amit Dubey, Andy Cedilnik, and Kyu-Young Kim. 2019 · 2019
Cited alongside, same era.
BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Cited alongside, same era.
An evaluation dataset for intent classification and out-of-scope prediction
Stefan Larson, Anish Mahendran, Joseph J. Peper, Christopher Clarke, Andrew Lee, Parker Hill, Jonathan K. Kummerfeld, Kevin Leach, Michael A. Laurenzano, Lingjia Tang, and Jason Mars. 2019 · 2019
Cited alongside, same era.
Towards scalable multi-domain conversational agents: The schema-guided dialogue dataset
Abhinav Rastogi, Xiaoxue Zang, Srinivas Sunkara, Raghav Gupta, and Pranav Khaitan. 2020 · 2020
Later among the works it cites.
Tod-bert: Pre-trained natural language understanding for task-oriented dialogue
Chien-Sheng Wu, Steven C. H. Hoi, Richard Socher, and Caiming Xiong. 2020 · 2020
Later among the works it cites.
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 William B. Dolan. 2020 · 2020
Later among the works it cites.
Peco: Perceptual codebook for bert pre-training of vision transformers
Xiaoyi Dong, Jianmin Bao, Ting Zhang, Dongdong Chen, Weiming Zhang, Lu Yuan, Dong Chen, Fang Wen, and Nenghai Yu. 2021 · 2021
Later among the works it cites.
SimCSE: Simple contrastive learning of sentence embeddings
Tianyu Gao, Xingcheng Yao, and Danqi Chen. 2021 · 2021
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Multi-domain task-completion dialog challenge
Sungjin Lee, Hannes Schulz, Adam Atkinson, Jianfeng Gao, Kaheer Suleman, Layla El Asri, Mahmoud Adada, Minlie Huang, Shikhar Sharma, Wendy Tay, and Xiujun Li. 2019 · 2019
Cited alongside, same era.
Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
Cited alongside, same era.
Towards empathetic open-domain conversation models: A new benchmark and dataset
Hannah Rashkin, Eric Michael Smith, Margaret Li, and Y-Lan Boureau. 2019 · 2019
Cited alongside, same era.
A survey on semi-supervised learning
Jesper E. van Engelen and Holger H. Hoos. 2019 · 2019
Cited alongside, same era.
Aligning books and movies: Towards story-like visual explanations by watching movies and reading books
Yukun Zhu, Ryan Kiros, Richard S. Zemel, Ruslan Salakhutdinov, Raquel Urtasun, Antonio Torralba, and Sanja Fidler. 2015 · 2019
Cited alongside, same era.
A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020 · 2020
Cited alongside, same era.
Bootstrap your own latent-a new approach to self-supervised learning
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, et al. 2020 · 2020
Cited alongside, same era.
Later among the works it cites.
Dialoguecse: Dialogue-based contrastive learning of sentence embeddings
Che Liu, Rui Wang, Jinghua Liu, Jian Sun, Fei Huang, and Luo Si. 2021 · 2021
Later among the works it cites.
Understanding the behaviour of contrastive loss
Feng Wang and Huaping Liu. 2021 · 2021
Later among the works it cites.
data2vec: A general framework for self-supervised learning in speech, vision and language
Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu, and Michael Auli. 2022 · 2022
Later among the works it cites.
Incremental false negative detection for contrastive learning
Tsai-Shien Chen, Wei-Chih Hung, Hung-Yu Tseng, Shao-Yi Chien, and Ming-Hsuan Yang. 2022 · 2022
Later among the works it cites.
Masked autoencoders are scalable vision learners
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Doll’ar, and Ross B. Girshick. 2022a · 2022
Later among the works it cites.
Boosting contrastive self-supervised learning with false negative cancellation
Tri Huynh, Simon Kornblith, Matthew R. Walter, Michael Maire, and Maryam Khademi. 2022 · 2022
Later among the works it cites.
Exploring target representations for masked autoencoders
Xingbin Liu, Jinghao Zhou, Tao Kong, Xianming Lin, and Rongrong Ji. 2022 · 2022
Later among the works it cites.
Learning dialogue representations from consecutive utterances
Zhihan Zhou, Dejiao Zhang, Wei Xiao, Nicholas Dingwall, Xiaofei Ma, Andrew O. Arnold, and Bing Xiang. 2022 · 2022
Later among the works it cites.