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We introduce CM3, a family of causally masked generative models trained over a large corpus of structured multi-modal documents that can contain both text and image tokens.
Rouge: A package for automatic evaluation of summaries
Chin-Yew Lin · 2004
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Robust disambiguation of named entities in text
Johannes Hoffart, Mohamed Amir Yosef, Ilaria Bordino, Hagen Fürstenau, Manfred Pinkal, Marc Spaniol, Bilyana Taneva, Stefan Thater, and Gerhard Weikum · 2011
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Kore: keyphrase overlap relatedness for entity disambiguation
Johannes Hoffart, Stephan Seufert, Dat Ba Nguyen, Martin Theobald, and Gerhard Weikum · 2012
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Annotated gigaword
Courtney Napoles, Matthew R Gormley, and Benjamin Van Durme · 2012
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Semantic multimedia information retrieval based on contextual descriptions
Nadine Steinmetz and Harald Sack · 2013
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Adam: A method for stochastic optimization
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Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
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Entity linking meets word sense disambiguation: a unified approach
Andrea Moro, Alessandro Raganato, and Roberto Navigli · 2014
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Zipf’s word frequency law in natural language: A critical review and future directions
Steven T Piantadosi · 2014
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N 3 -a collection of datasets for named entity recognition and disambiguation in the nlp interchange format
Michael Röder, Ricardo Usbeck, Sebastian Hellmann, Daniel Gerber, and Andreas Both · 2014
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Analysis of named entity recognition and linking for tweets
Leon Derczynski, Diana Maynard, Giuseppe Rizzo, Marieke Van Erp, Genevieve Gorrell, Raphaël Troncy, Johann Petrak, and Kalina Bontcheva · 2015
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Teaching machines to read and comprehend
Karl Moritz Hermann, Tomas Kocisky, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom · 2015
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Open knowledge extraction challenge
Andrea Giovanni Nuzzolese, Anna Lisa Gentile, Valentina Presutti, Aldo Gangemi, Darío Garigliotti, and Roberto Navigli · 2015
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Semantics derived automatically from language corpora contain human-like biases
Aylin Caliskan, Joanna J. Bryson, and Arvind Narayanan · 2017
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Deep joint entity disambiguation with local neural attention
Octavian-Eugen Ganea and Thomas Hofmann · 2017
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2017
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Attngan: Fine-grained text to image generation with attentional generative adversarial networks
Tao Xu, Pengchuan Zhang, Qiuyuan Huang, Han Zhang, Zhe Gan, Xiaolei Huang, and Xiaodong He · 2017
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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Robust named entity disambiguation with random walks
Zhaochen Guo and Denilson Barbosa · 2018
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Abstractive summarization of reddit posts with multi-level memory networks
Byeongchang Kim, Hyunwoo Kim, and Gunhee Kim · 2018
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End-to-end neural entity linking
Nikolaos Kolitsas, Octavian-Eugen Ganea, and Thomas Hofmann · 2018
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Improving entity linking by modeling latent relations between mentions
Phong Le and Ivan Titov · 2018
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Shashi Narayan, Shay B Cohen, and Mirella Lapata · 2018
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Gerbil–benchmarking named entity recognition and linking consistently
Michael Röder, Ricardo Usbeck, and Axel-Cyrille Ngonga Ngomo · 2018
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GLUE: A multi-task benchmark and analysis platform for natural language understanding
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel Bowman · 2018
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Collective entity disambiguation with structured gradient tree boosting
Yi Yang, Ozan Irsoy, and Kazi Shefaet Rahman · 2018
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vq-wav2vec: Self-supervised learning of discrete speech representations
Alexei Baevski, Steffen Schneider, and Michael Auli · 2019
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Joint entity linking with deep reinforcement learning
Zheng Fang, Yanan Cao, Qian Li, Dongjie Zhang, Zhenyu Zhang, and Yanbing Liu · 2019
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Better fine-tuning by reducing representational collapse
Armen Aghajanyan, Akshat Shrivastava, Anchit Gupta, Naman Goyal, Luke Zettlemoyer, and Sonal Gupta · 2020
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Investigating entity knowledge in bert with simple neural end-to-end entity linking
Samuel Broscheit · 2020
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Language models are few-shot learners
Tom B Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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Autoregressive entity retrieval
Nicola De Cao, Gautier Izacard, Sebastian Riedel, and Fabio Petroni · 2020
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Boosting entity linking performance by leveraging unlabeled documents
Phong Le and Ivan Titov · 2019
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Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov, and Luke Zettlemoyer · 2019
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Visualbert: A simple and performant baseline for vision and language
Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, and Kai-Wei Chang · 2019
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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
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Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks
Jiasen Lu, Dhruv Batra, Devi Parikh, and Stefan Lee · 2019
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Joint learning of named entity recognition and entity linking
Pedro Henrique Martins, Zita Marinho, and André FT Martins · 2019
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On measuring social biases in sentence encoders
Chandler May, Alex Wang, Shikha Bordia, Samuel R. Bowman, and Rachel Rudinger · 2019
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Prafulla Dhariwal, Heewoo Jun, Christine Payne, Jong Wook Kim, Alec Radford, and Ilya Sutskever · 2020
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Alexander R Fabbri, Simeng Han, Haoyuan Li, Haoran Li, Marjan Ghazvininejad, Shafiq Joty, Dragomir Radev, and Yashar Mehdad · 2020
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Scaling laws for neural language models
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei · 2020
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pytorch-fid: FID Score for PyTorch
Maximilian Seitzer · 2020
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Df-gan: Deep fusion generative adversarial networks for text-to-image synthesis
Ming Tao, Hao Tang, Songsong Wu, Nicu Sebe, Xiao-Yuan Jing, Fei Wu, and Bingkun Bao · 2020
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Rel: An entity linker standing on the shoulders of giants
Johannes M van Hulst, Faegheh Hasibi, Koen Dercksen, Krisztian Balog, and Arjen P de Vries · 2020
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Htlm: Hyper-text pre-training and prompting of language models
Armen Aghajanyan, Dmytro Okhonko, Mike Lewis, Mandar Joshi, Hu Xu, Gargi Ghosh, and Luke Zettlemoyer · 2021
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Efficient large scale language modeling with mixtures of experts
Mikel Artetxe, Shruti Bhosale, Naman Goyal, Todor Mihaylov, Myle Ott, Sam Shleifer, Xi Victoria Lin, Jingfei Du, Srinivasan Iyer, Ramakanth Pasunuru, et al · 2021
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Fairscale: A general purpose modular pytorch library for high performance and large scale training
Mandeep Baines, Shruti Bhosale, Vittorio Caggiano, Naman Goyal, Siddharth Goyal, Myle Ott, Benjamin Lefaudeux, Vitaliy Liptchinsky, Mike Rabbat, Sam Sheiffer, Anjali Sridhar, and Min Xu · 2021
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Multimodal datasets: misogyny, pornography, and malignant stereotypes
Abeba Birhane, Vinay Uday Prabhu, and Emmanuel Kahembwe · 2021
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Taming transformers for high-resolution image synthesis
Patrick Esser, Robin Rombach, and Bjorn Ommer · 2021
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Glide: Towards photorealistic image generation and editing with text-guided diffusion models
Alex Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob McGrew, Ilya Sutskever, and Mark Chen · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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Zero-shot text-to-image generation
Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever · 2021
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Measuring social biases in grounded vision and language embeddings
Candace Ross, Boris Katz, and Andrei Barbu · 2021
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Improving text-to-image synthesis using contrastive learning
Hui Ye, Xiulong Yang, Martin Takac, Rajshekhar Sunderraman, and Shihao Ji · 2021
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Cross-modal contrastive learning for text-to-image generation
Han Zhang, Jing Yu Koh, Jason Baldridge, Honglak Lee, and Yinfei Yang · 2021
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Lafite: Towards language-free training for text-to-image generation
Yufan Zhou, Ruiyi Zhang, Changyou Chen, Chunyuan Li, Chris Tensmeyer, Tong Yu, Jiuxiang Gu, Jinhui Xu, and Tong Sun · 2021
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