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Models pre-trained with a language modeling objective possess ample world knowledge and language skills, but are known to struggle in tasks that require reasoning.
UNIFIEDQA: Crossing format boundaries with a single QA system
Daniel Khashabi, Sewon Min, Tushar Khot, Ashish Sabharwal, Oyvind Tafjord, Peter Clark, and Hannaneh Hajishirzi. 2020 · 1907
Earlier work this paper cites.
Overcoming catastrophic forgetting in neural networks
James Kirkpatrick, Razvan Pascanu, Neil C. Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A. Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, Demis Hassabis, Claudia Clopath, Dharshan Kumaran, and Raia Hadsell. 2016 · 2016
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Automated curriculum learning for neural networks
Alex Graves, Marc G. Bellemare, Jacob Menick, Rémi Munos, and Koray Kavukcuoglu. 2017 · 2017
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Learning to multi-task by active sampling
Sahil Sharma, Ashutosh Kumar Jha, Parikshit Hegde, and Balaraman Ravindran. 2018 · 2018
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Fast and accurate reading comprehension by combining self-attention and convolution
Adams Wei Yu, David Dohan, Quoc Le, Thang Luong, Rui Zhao, and Kai Chen. 2018 · 2018
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Synthetic QA corpora generation with roundtrip consistency
Chris Alberti, Daniel Andor, Emily Pitler, Jacob Devlin, and Michael Collins. 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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DROP: A reading comprehension benchmark requiring discrete reasoning over paragraphs
Dheeru Dua, Yizhong Wang, Pradeep Dasigi, Gabriel Stanovsky, Sameer Singh, and Matt Gardner. 2019 · 2019
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Tablenet: An approach for determining fine-grained relations for wikipedia tables
Besnik Fetahu, Avishek Anand, and Maria Koutraki. 2019 · 2019
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Task selection policies for multitask learning
John Glover and Chris Hokamp. 2019 · 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 · 2019
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Distributionally robust language modeling
Yonatan Oren, Shiori Sagawa, Tatsunori Hashimoto, and Percy Liang. 2019 · 2019
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NumNet: Machine reading comprehension with numerical reasoning
Qiu Ran, Yankai Lin, Peng Li, Jie Zhou, and Zhiyuan Liu. 2019 · 2019
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Diversify your datasets: Analyzing generalization via controlled variance in adversarial datasets
Ohad Rozen, Vered Shwartz, Roee Aharoni, and Ido Dagan. 2019 · 2019
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MultiQA: An empirical investigation of generalization and transfer in reading comprehension
Alon Talmor and Jonathan Berant. 2019 · 2019
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CommonsenseQA: A question answering challenge targeting commonsense knowledge
Alon Talmor, Jonathan Herzig, Nicholas Lourie, and Jonathan Berant. 2019 · 2019
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Do NLP models know numbers? probing numeracy in embeddings
Eric Wallace, Yizhong Wang, Sujian Li, Sameer Singh, and Matt Gardner. 2019 · 2019
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Investigating BERT’s knowledge of language: Five analysis methods with NPIs
Alex Warstadt, Yu Cao, Ioana Grosu, Wei Peng, Hagen Blix, Yining Nie, Anna Alsop, Shikha Bordia, Haokun Liu, Alicia Parrish, Sheng-Fu Wang, Jason Phang, Anhad Mohananey, Phu Mon Htut, Paloma Jeretic, and Samuel R. Bowman. 2019 · 2019
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Multi-task learning with sample re-weighting for machine reading comprehension
Yichong Xu, Xiaodong Liu, Yelong Shen, Jingjing Liu, and Jianfeng Gao. 2019 · 2019
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Learning and evaluating general linguistic intelligence
Dani Yogatama, Cyprien de Masson d’Autume, Jerome Connor, Tomas Kocisky, Mike Chrzanowski, Lingpeng Kong, Angeliki Lazaridou, Wang Ling, Lei Yu, Chris Dyer, and Phil Blunsom. 2019 · 2019
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Data augmentation with atomic templates for spoken language understanding
Zijian Zhao, Su Zhu, and Kai Yu. 2019 · 2019
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Good-enough compositional data augmentation
Jacob Andreas. 2020 · 2020
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Logic-guided data augmentation and regularization for consistent question answering
Akari Asai and Hannaneh Hajishirzi. 2020 · 2020
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Learning to retrieve reasoning paths over wikipedia graph for question answering
Akari Asai, Kazuma Hashimoto, Hannaneh Hajishirzi, Richard Socher, and Caiming Xiong. 2020 · 2020
Cited alongside, same era.
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, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020 · 2020
Cited alongside, same era.
oLMpics-on what language model pre-training captures
Alon Talmor, Yanai Elazar, Yoav Goldberg, and Jonathan Berant. 2020 · 2020
Later among the works it cites.
Balancing training for multilingual neural machine translation
Xinyi Wang, Yulia Tsvetkov, and Graham Neubig. 2020 · 2020
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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, 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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TaBERT: Pretraining for joint understanding of textual and tabular data
Pengcheng Yin, Graham Neubig, Wen-tau Yih, and Sebastian Riedel. 2020 · 2020
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Worst-case-aware curriculum learning for zero and few shot transfer
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Zero-shot transfer learning with synthesized data for multi-domain dialogue state tracking
Giovanni Campagna, Agata Foryciarz, Mehrad Moradshahi, and Monica Lam. 2020 · 2020
Cited alongside, same era.
Question directed graph attention network for numerical reasoning over text
Kunlong Chen, Weidi Xu, Xingyi Cheng, Zou Xiaochuan, Yuyu Zhang, Le Song, Taifeng Wang, Yuan Qi, and Wei Chu. 2020a · 2020
Cited alongside, same era.
Tabfact: A large-scale dataset for table-based fact verification
Wenhu Chen, Hongmin Wang, Jianshu Chen, Yunkai Zhang, Hong Wang, Shiyang Li, Xiyou Zhou, and William Yang Wang. 2020b · 2020
Cited alongside, same era.
Neural symbolic reader: Scalable integration of distributed and symbolic representations for reading comprehension
Xinyun Chen, Chen Liang, Adams Wei Yu, Denny Zhou, Dawn Song, and Quoc V. Le. 2020c · 2020
Cited alongside, same era.
Understanding tables with intermediate pre-training
Julian Eisenschlos, Syrine Krichene, and Thomas Müller. 2020 · 2020
Cited alongside, same era.
IIRC: A dataset of incomplete information reading comprehension questions
James Ferguson, Matt Gardner, Hannaneh Hajishirzi, Tushar Khot, and Pradeep Dasigi. 2020 · 2020
Cited alongside, same era.
Injecting numerical reasoning skills into language models
Mor Geva, Ankit Gupta, and Jonathan Berant. 2020 · 2020
Cited alongside, same era.
Sheng Zhang, Xin Zhang, Weiming Zhang, and Anders Søgaard. 2020 · 2020
Later among the works it cites.
Improving question answering model robustness with synthetic adversarial data generation
Max Bartolo, Tristan Thrush, Robin Jia, Sebastian Riedel, Pontus Stenetorp, and Douwe Kiela. 2021 · 2021
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A survey of data augmentation approaches for NLP
Steven Y. Feng, Varun Gangal, Jason Wei, Sarath Chandar, Soroush Vosoughi, Teruko Mitamura, and Eduard Hovy. 2021 · 2021
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Text modular networks: Learning to decompose tasks in the language of existing models
Tushar Khot, Daniel Khashabi, Kyle Richardson, Peter Clark, and Ashish Sabharwal. 2021 · 2021
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Tapas at semeval-2021 task 9: Reasoning over tables with intermediate pre-training
Thomas Müller, Julian Martin Eisenschlos, and Syrine Krichene. 2021 · 2021
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Fetaqa: Free-form table question answering
Linyong Nan, Chiachun Hsieh, Ziming Mao, Xi Victoria Lin, Neha Verma, Rui Zhang, Wojciech Kryscinski, Nick Schoelkopf, Riley Kong, Xiangru Tang, Murori Mutuma, Ben Rosand, Isabel Trindade, Renusree Bandaru, Jacob Cunningham, Caiming Xiong, and Dragomir R. Radev. 2021 · 2021
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Incorporating external knowledge to enhance tabular reasoning
J. Neeraja, Vivek Gupta, and Vivek Srikumar. 2021a · 2021
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Incorporating external knowledge to enhance tabular reasoning
J. Neeraja, Vivek Gupta, and Vivek Srikumar. 2021b · 2021
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Ansong Ni, Matt Gardner, and Pradeep Dasigi. 2021 · 2021
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Conditionally adaptive multi-task learning: Improving transfer learning in NLP using fewer parameters & less data
Jonathan Pilault, Amine El hattami, and Christopher Pal. 2021 · 2021
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Few-shot question answering by pretraining span selection
Ori Ram, Yuval Kirstain, Jonathan Berant, Amir Globerson, and Omer Levy. 2021 · 2021
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MultimodalQA: complex question answering over text, tables and images
Alon Talmor, Ori Yoran, Amnon Catav, Dan Lahav, Yizhong Wang, Akari Asai, Gabriel Ilharco, Hannaneh Hajishirzi, and Jonathan Berant. 2021 · 2021
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Representing numbers in NLP: a survey and a vision
Avijit Thawani, Jay Pujara, Filip Ilievski, and Pedro Szekely. 2021 · 2021
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James Thorne, Majid Yazdani, Marzieh Saeidi, Fabrizio Silvestri, Sebastian Riedel, and Alon Y. Halevy. 2021 · 2021
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Nt5?! training t5 to perform numerical reasoning
Peng-Jian Yang, Ying Ting Chen, Yuechan Chen, and Daniel Cer. 2021 · 2021
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GraPPa: Grammar-augmented pre-training for table semantic parsing
Tao Yu, Chien-Sheng Wu, Xi Victoria Lin, bailin wang, Yi Chern Tan, Xinyi Yang, Dragomir Radev, richard socher, and Caiming Xiong. 2021 · 2021
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