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Intermediate task fine-tuning has been shown to culminate in large transfer gains across many NLP tasks.
Introduction to the CoNLL-2000 shared task chunking
Erik F. Tjong Kim Sang and Sabine Buchholz. 2000 · 2000
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Cumulated gain-based evaluation of IR techniques
Kalervo Järvelin and Jaana Kekäläinen. 2002 · 2002
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Learning question classifiers
Xin Li and Dan Roth. 2002 · 2002
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Introduction to the CoNLL-2003 shared task: Language-independent named entity recognition
Erik F. Tjong Kim Sang and Fien De Meulder. 2003 · 2003
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The PASCAL recognising textual entailment challenge
Ido Dagan, Oren Glickman, and Bernardo Magnini. 2005 · 2005
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Automatically constructing a corpus of sentential paraphrases
William B. Dolan and Chris Brockett. 2005 · 2005
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Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales
Bo Pang and Lillian Lee. 2005 · 2005
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Reciprocal rank fusion outperforms condorcet and individual rank learning methods
Gordon V. Cormack, Charles L. A. Clarke, and Stefan Büttcher. 2009 · 2009
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Learning word vectors for sentiment analysis
Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts. 2011 · 2011
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SemEval-2012 task 7: Choice of plausible alternatives: An evaluation of commonsense causal reasoning
Andrew S. Gordon, Zornitsa Kozareva, and Melissa Roemmele. 2012 · 2012
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Recursive deep models for semantic compositionality over a sentiment treebank
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D. Manning, Andrew Y. Ng, and Christopher Potts. 2013 · 2013
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Auto-encoding variational bayes
Diederik P. Kingma and Max Welling. 2014 · 2014
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A SICK cure for the evaluation of compositional distributional semantic models
Marco Marelli, Stefano Menini, Marco Baroni, Luisa Bentivogli, Raffaella Bernardi, and Roberto Zamparelli. 2014 · 2014
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A gold standard dependency corpus for english
Natalia Silveira, Timothy Dozat, Marie-Catherine de Marneffe, Samuel R. Bowman, Miriam Connor, John Bauer, and Christopher D. Manning. 2014 · 2014
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A large annotated corpus for learning natural language inference
Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015 · 2015
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Character-level convolutional networks for text classification
Xiang Zhang, Junbo Jake Zhao, and Yann LeCun. 2015 · 2015
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Constraint-based question answering with knowledge graph
Junwei Bao, Nan Duan, Zhao Yan, Ming Zhou, and Tiejun Zhao. 2016 · 2016
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SQuAD: 100, 000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
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Compositional sequence labeling models for error detection in learner writing
Marek Rei and Helen Yannakoudakis. 2016 · 2016
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Towards universal semantic tagging
Lasha Abzianidze and Johan Bos. 2017 · 2017
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Identifying beneficial task relations for multi-task learning in deep neural networks
Joachim Bingel and Anders Søgaard. 2017 · 2017
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SemEval-2017 task 1: Semantic textual similarity multilingual and crosslingual focused evaluation
Daniel M. Cer, Mona T. Diab, Eneko Agirre, Iñigo Lopez-Gazpio, and Lucia Specia. 2017 · 2017
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Results of the WNUT2017 shared task on novel and emerging entity recognition
Leon Derczynski, Eric Nichols, Marieke van Erp, and Nut Limsopatham. 2017 · 2017
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Towards a neural statistician
Harrison Edwards and Amos J. Storkey. 2017 · 2017
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First Quora Dataset Release: Question Pairs
Shankar Iyer, Nikhil Dandekar, and Kornél Csernai. 2017 · 2017
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RACE: Large-scale ReAding comprehension dataset from examinations
Guokun Lai, Qizhe Xie, Hanxiao Liu, Yiming Yang, and Eduard H. Hovy. 2017 · 2017
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NewsQA: A machine comprehension dataset
Adam Trischler, Tong Wang, Xingdi Yuan, Justin Harris, Alessandro Sordoni, Philip Bachman, and Kaheer Suleman. 2017 · 2017
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Looking beyond the surface: A challenge set for reading comprehension over multiple sentences
Daniel Khashabi, Snigdha Chaturvedi, Michael Roth, Shyam Upadhyay, and Dan Roth. 2018 · 2018
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SciTaiL: A textual entailment dataset from science question answering
Tushar Khot, Ashish Sabharwal, and Peter Clark. 2018 · 2018
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Sentence encoders on STILTs: Supplementary training on intermediate labeled-data tasks
Jason Phang, Thibault Févry, and Samuel R. Bowman. 2018 · 2018
Cited alongside, same era.
Know what you don’t know: Unanswerable questions for SQuAD
Pranav Rajpurkar, Robin Jia, and Percy Liang. 2018 · 2018
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DuoRC: Towards complex language understanding with paraphrased reading comprehension
Amrita Saha, Rahul Aralikatte, Mitesh M. Khapra, and Karthik Sankaranarayanan. 2018 · 2018
Cited alongside, same era.
CARER: Contextualized affect representations for emotion recognition
Elvis Saravia, Hsien-Chi Toby Liu, Yen-Hao Huang, Junlin Wu, and Yi-Shin Chen. 2018 · 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 R. Bowman. 2018 · 2018
Cited alongside, same era.
Can you tell me how to get past sesame street? Sentence-level pretraining beyond language modeling
Alex Wang, Jan Hula, Patrick Xia, Raghavendra Pappagari, R. Thomas McCoy, Roma Patel, Najoung Kim, Ian Tenney, Yinghui Huang, Katherin Yu, Shuning Jin, Berlin Chen, Benjamin Van Durme, Edouard Grave, Ellie Pavlick, and Samuel R. Bowman. 2019a · 2019
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SuperGLUE: A stickier benchmark for general-purpose language understanding systems
Alex Wang, Yada Pruksachatkun, Nikita Nangia, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R. Bowman. 2019b · 2019
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Neural network acceptability judgments
Alex Warstadt, Amanpreet Singh, and Samuel R. Bowman. 2019 · 2019
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Learning and evaluating general linguistic intelligence
Dani Yogatama, Cyprien de Masson d’Autume, Jerome Connor, Tomás Kociský, Mike Chrzanowski, Lingpeng Kong, Angeliki Lazaridou, Wang Ling, Lei Yu, Chris Dyer, and Phil Blunsom. 2019 · 2019
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HellaSwag: Can a machine really finish your sentence?
Rowan Zellers, Ari Holtzman, Yonatan Bisk, Ali Farhadi, and Yejin Choi. 2019 · 2019
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Constructing datasets for multi-hop reading comprehension across documents
Johannes Welbl, Pontus Stenetorp, and Sebastian Riedel. 2018 · 2018
Cited alongside, same era.
A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel R. Bowman. 2018 · 2018
Cited alongside, same era.
HotpotQA: A dataset for diverse, explainable multi-hop question answering
Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William W. Cohen, Ruslan Salakhutdinov, and Christopher D. Manning. 2018 · 2018
Cited alongside, same era.
Taskonomy: Disentangling task transfer learning
Amir Roshan Zamir, Alexander Sax, William B. Shen, Leonidas J. Guibas, Jitendra Malik, and Silvio Savarese. 2018 · 2018
Cited alongside, same era.
SWAG: A large-scale adversarial dataset for grounded commonsense inference
Rowan Zellers, Yonatan Bisk, Roy Schwartz, and Yejin Choi. 2018 · 2018
Cited alongside, same era.
ReCoRD: Bridging the gap between human and machine commonsense reading comprehension
Sheng Zhang, Xiaodong Liu, Jingjing Liu, Jianfeng Gao, Kevin Duh, and Benjamin Van Durme. 2018 · 2018
Cited alongside, same era.
Task2Vec: Task embedding for meta-learning
Alessandro Achille, Michael Lam, Rahul Tewari, Avinash Ravichandran, Subhransu Maji, Charless C. Fowlkes, Stefano Soatto, and Pietro Perona. 2019 · 2019
Cited alongside, same era.
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Abductive commonsense reasoning
Chandra Bhagavatula, Ronan Le Bras, Chaitanya Malaviya, Keisuke Sakaguchi, Ari Holtzman, Hannah Rashkin, Doug Downey, Wen-tau Yih, and Yejin Choi. 2020 · 2020
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Don’t stop pretraining: Adapt language models to domains and tasks
Suchin Gururangan, Ana Marasovic, Swabha Swayamdipta, Kyle Lo, Iz Beltagy, Doug Downey, and Noah A. Smith. 2020 · 2020
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The state and fate of linguistic diversity and inclusion in the NLP world
Pratik Joshi, Sebastin Santy, Amar Budhiraja, Kalika Bali, and Monojit Choudhury. 2020 · 2020
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Proceedings of SustaiNLP: Workshop on Simple and Efficient Natural Language Processing . Association for Computational Linguistics, Online
Nafise Sadat Moosavi, Angela Fan, Vered Shwartz, Goran Glavaš, Shafiq Joty, Alex Wang, and Thomas Wolf, editors. 2020 · 2020
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LEEP: A new measure to evaluate transferability of learned representations
Cuong Nguyen, Tal Hassner, Matthias W. Seeger, and Cédric Archambeau. 2020 · 2020
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Adversarial NLI: A new benchmark for natural language understanding
Yixin Nie, Adina Williams, Emily Dinan, Mohit Bansal, Jason Weston, and Douwe Kiela. 2020 · 2020
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AdapterHub: A framework for adapting transformers
Jonas Pfeiffer, Andreas Rücklé, Clifton Poth, Aishwarya Kamath, Ivan Vulic, Sebastian Ruder, Kyunghyun Cho, and Iryna Gurevych. 2020a · 2020
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MAD-X: An Adapter-Based Framework for Multi-Task Cross-Lingual Transfer
Jonas Pfeiffer, Ivan Vulić, Iryna Gurevych, and Sebastian Ruder. 2020b · 2020
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Intermediate-task transfer learning with pretrained language models: When and why does it work?
Yada Pruksachatkun, Jason Phang, Haokun Liu, Phu Mon Htut, Xiaoyi Zhang, Richard Yuanzhe Pang, Clara Vania, Katharina Kann, and Samuel R. Bowman. 2020 · 2020
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Which model to transfer? Finding the needle in the growing haystack
Cédric Renggli, André Susano Pinto, Luka Rimanic, Joan Puigcerver, Carlos Riquelme, Ce Zhang, and Mario Lucic. 2020 · 2020
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Getting closer to AI complete question answering: A set of prerequisite real tasks
Anna Rogers, Olga Kovaleva, Matthew Downey, and Anna Rumshisky. 2020 · 2020
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MultiCQA: Zero-shot transfer of self-supervised text matching models on a massive scale
Andreas Rücklé, Jonas Pfeiffer, and Iryna Gurevych. 2020 · 2020
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WinoGrande: An adversarial winograd schema challenge at scale
Keisuke Sakaguchi, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi. 2020 · 2020
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UDapter: Language Adaptation for Truly Universal Dependency Parsing
Ahmet Üstün, Arianna Bisazza, Gosse Bouma, and Gertjan van Noord. 2020 · 2020
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Orthogonal language and task adapters in zero-shot cross-lingual transfer
Marko Vidoni, Ivan Vulić, and Goran Glavaš. 2020 · 2020
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Exploring and predicting transferability across NLP tasks
Tu Vu, Tong Wang, Tsendsuren Munkhdalai, Alessandro Sordoni, Adam Trischler, Andrew Mattarella-Micke, Subhransu Maji, and Mohit Iyyer. 2020 · 2020
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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, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander M. Rush. 2020 · 2020
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MAD-G: Multilingual Adapter Generation for Efficient Cross-Lingual Transfer
Alan Ansell, Edoardo Maria Ponti, Jonas Pfeiffer, Sebastian Ruder, Goran Glavaš, Ivan Vulić, and Anna Korhonen. 2021 · 2021
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A linearized framework and a new benchmark for model selection for fine-tuning
Aditya Deshpande, Alessandro Achille, Avinash Ravichandran, Hao Li, Luca Zancato, Charless C. Fowlkes, Rahul Bhotika, Stefano Soatto, and Pietro Perona. 2021 · 2021
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Ranking neural checkpoints
Yandong Li, Xuhui Jia, Ruoxin Sang, Yukun Zhu, Bradley Green, Liqiang Wang, and Boqing Gong. 2021 · 2021
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UNKs Everywhere: Adapting Multilingual Language Models to New Scripts
Jonas Pfeiffer, Ivan Vulić, Iryna Gurevych, and Sebastian Ruder. 2021b · 2021
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Scalable transfer learning with expert models
Joan Puigcerver, Carlos Riquelme Ruiz, Basil Mustafa, Cédric Renggli, André Susano Pinto, Sylvain Gelly, Daniel Keysers, and Neil Houlsby. 2021 · 2021
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AdapterDrop: On the Efficiency of Adapters in Transformers
Andreas Rücklé, Gregor Geigle, Max Glockner, Tilman Beck, Jonas Pfeiffer, Nils Reimers, and Iryna Gurevych. 2021 · 2021
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How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models
Phillip Rust, Jonas Pfeiffer, Ivan Vulić, Sebastian Ruder, and Iryna Gurevych. 2021 · 2021
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