Fetching the paper…
Reading the bibliography…
Recently, contrastive learning attracts increasing interests in neural text generation as a new solution to alleviate the exposure bias problem.
Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu · 2002
Earlier work this paper cites.
Visualizing data using t-sne
Laurens Van der Maaten and Geoffrey Hinton · 2008
Earlier work this paper cites.
Sequence to sequence learning with neural networks, 2014
Ilya Sutskever, Oriol Vinyals, and Quoc V. Le · 2014
Earlier work this paper cites.
Learning fine-grained image similarity with deep ranking
Jiang Wang, Yang Song, Thomas Leung, Chuck Rosenberg, Jingbin Wang, James Philbin, Bo Chen, and Ying Wu · 2014
Earlier work this paper cites.
Scheduled sampling for sequence prediction with recurrent neural networks
Samy Bengio, Oriol Vinyals, Navdeep Jaitly, and Noam Shazeer · 2015
Earlier work this paper cites.
Facenet: A unified embedding for face recognition and clustering
Florian Schroff, Dmitry Kalenichenko, and James Philbin · 2015
Earlier work this paper cites.
Cider: Consensus-based image description evaluation
Ramakrishna Vedantam, C Lawrence Zitnick, and Devi Parikh · 2015
Earlier work this paper cites.
Spice: Semantic propositional image caption evaluation
Peter Anderson, Basura Fernando, Mark Johnson, and Stephen Gould · 2016
Earlier work this paper cites.
Neural summarization by extracting sentences and words
Jianpeng Cheng and Mirella Lapata · 2016
Earlier work this paper cites.
Neural text generation from structured data with application to the biography domain
Rémi Lebret, David Grangier, and Michael Auli · 2016
Earlier work this paper cites.
Improved deep metric learning with multi-class n-pair loss objective
Kihyuk Sohn · 2016
Earlier work this paper cites.
Diverse beam search: Decoding diverse solutions from neural sequence models
Ashwin K Vijayakumar, Michael Cogswell, Ramprasath R Selvaraju, Qing Sun, Stefan Lee, David Crandall, and Dhruv Batra · 2016
Earlier work this paper cites.
On integrating a language model into neural machine translation
Caglar Gulcehre, Orhan Firat, Kelvin Xu, Kyunghyun Cho, and Yoshua Bengio · 2017
Earlier work this paper cites.
A deep reinforced model for abstractive summarization
Romain Paulus, Caiming Xiong, and Richard Socher · 2017
Earlier work this paper cites.
Neural discrete representation learning
Aaron Van Den Oord, Oriol Vinyals, et al · 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
Earlier work this paper cites.
Learning deep representations by mutual information estimation and maximization
R Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, Karan Grewal, Philip Bachman, Adam Trischler, and Yoshua Bengio · 2018
Earlier work this paper cites.
Table-to-text generation by structure-aware seq2seq learning
Tianyu Liu, Kexiang Wang, Lei Sha, Baobao Chang, and Zhifang Sui · 2018
Earlier work this paper cites.
Don’t give me the details, just the summary! topic-aware convolutional neural networks for extreme summarization
Shashi Narayan, Shay B Cohen, and Mirella Lapata · 2018
Earlier work this paper cites.
Representation learning with contrastive predictive coding
Aaron Van den Oord, Yazhe Li, and Oriol Vinyals · 2018
Earlier work this paper cites.
Energy confused adversarial metric learning for zero-shot image retrieval and clustering
Binghui Chen and Weihong Deng · 2019
Cited alongside, same era.
Handling divergent reference texts when evaluating table-to-text generation
Bhuwan Dhingra, Manaal Faruqui, Ankur Parikh, Ming-Wei Chang, Dipanjan Das, and William W Cohen · 2019
Cited alongside, same era.
Multi-news: A large-scale multi-document summarization dataset and abstractive hierarchical model
Alexander Richard Fabbri, Irene Li, Tianwei She, Suyi Li, and Dragomir Radev · 2019
Cited alongside, same era.
Deep metric learning beyond binary supervision
Sungyeon Kim, Minkyo Seo, Ivan Laptev, Minsu Cho, and Suha Kwak · 2019
Cited alongside, same era.
Facebook fair’s wmt19 news translation task submission
Nathan Ng, Kyra Yee, Alexei Baevski, Myle Ott, Michael Auli, and Sergey Edunov · 2019
Cited alongside, same era.
Contrastive learning with adversarial perturbations for conditional text generation
Seanie Lee, Dong Bok Lee, and Sung Ju Hwang · 2020
Later among the works it cites.
Commongen: A constrained text generation challenge for generative commonsense reasoning
Bill Yuchen Lin, Wangchunshu Zhou, Ming Shen, Pei Zhou, Chandra Bhagavatula, Yejin Choi, and Xiang Ren · 2020
Later among the works it cites.
Ssmba: Self-supervised manifold based data augmentation for improving out-of-domain robustness
Nathan Ng, Kyunghyun Cho, and Marzyeh Ghassemi · 2020
Later among the works it cites.
Totto: A controlled table-to-text generation dataset
Ankur Parikh, Xuezhi Wang, Sebastian Gehrmann, Manaal Faruqui, Bhuwan Dhingra, Diyi Yang, and Dipanjan Das · 2020
Later among the works it cites.
Contrastive learning with hard negative samples
Joshua Robinson, Ching-Yao Chuang, Suvrit Sra, and Stefanie Jegelka · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
fairseq: A fast, extensible toolkit for sequence modeling
Myle Ott, Sergey Edunov, Alexei Baevski, Angela Fan, Sam Gross, Nathan Ng, David Grangier, and Michael Auli · 2019
Cited alongside, same era.
Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu · 2019
Cited alongside, same era.
Julian Salazar, Davis Liang, Toan Q Nguyen, and Katrin Kirchhoff · 2019
Cited alongside, same era.
Huggingface’s transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, et al · 2019
Cited alongside, same era.
Xlnet: Generalized autoregressive pretraining for language understanding
Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Russ R Salakhutdinov, and Quoc V Le · 2019
Cited alongside, same era.
Simple and effective noisy channel modeling for neural machine translation
Kyra Yee, Yann Dauphin, and Michael Auli · 2019
Cited alongside, same era.
Bertscore: Evaluating text generation with bert
Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q Weinberger, and Yoav Artzi · 2019
Cited alongside, same era.
Bleurt: Learning robust metrics for text generation
Thibault Sellam, Dipanjan Das, and Ankur P Parikh · 2020
Later among the works it cites.
Pegasus: Pre-training with extracted gap-sentences for abstractive summarization
Jingqing Zhang, Yao Zhao, Mohammad Saleh, and Peter Liu · 2020
Later among the works it cites.
Extractive summarization as text matching
Ming Zhong, Pengfei Liu, Yiran Chen, Danqing Wang, Xipeng Qiu, and Xuanjing Huang · 2020
Later among the works it cites.
Ladder loss for coherent visual-semantic embedding
Mo Zhou, Zhenxing Niu, Le Wang, Zhanning Gao, Qilin Zhang, and Gang Hua · 2020
Later among the works it cites.
R2d2: Relational text decoding with transformers
Aryan Arbabi, Mingqiu Wang, Laurent El Shafey, Nan Du, and Izhak Shafran · 2021
Later among the works it cites.
Simcse: Simple contrastive learning of sentence embeddings
Tianyu Gao, Xingcheng Yao, and Danqi Chen · 2021
Later among the works it cites.
Deep ranking with adaptive margin triplet loss
Mai Lan Ha and Volker Blanz · 2021
Later among the works it cites.
Simcls: A simple framework for contrastive learning of abstractive summarization
Yixin Liu and Pengfei Liu · 2021
Later among the works it cites.
Codexglue: A machine learning benchmark dataset for code understanding and generation
Shuai Lu, Daya Guo, Shuo Ren, Junjie Huang, Alexey Svyatkovskiy, Ambrosio Blanco, Colin Clement, Dawn Drain, Daxin Jiang, Duyu Tang, et al · 2021
Later among the works it cites.
Retrieval augmented code generation and summarization
Md Rizwan Parvez, Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, and Kai-Wei Chang · 2021
Later among the works it cites.
Codet5: Identifier-aware unified pre-trained encoder-decoder models for code understanding and generation
Yue Wang, Weishi Wang, Shafiq Joty, and Steven CH Hoi · 2021
Later among the works it cites.
Rankgen: Improving text generation with large ranking models
Kalpesh Krishna, Yapei Chang, John Wieting, and Mohit Iyyer · 2022
Closest in time.
A contrastive framework for neural text generation
Yixuan Su, Tian Lan, Yan Wang, Dani Yogatama, Lingpeng Kong, and Nigel Collier · 2022
Closest in time.
KNN-contrastive learning for out-of-domain intent classification
Yunhua Zhou, Peiju Liu, and Xipeng Qiu · 2022
Closest in time.