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Transformer-based language models trained on large text corpora have enjoyed immense popularity in the natural language processing community and are commonly used as a starting point for downstream tasks.
A distance measure between attributed relational graphs for pattern recognition
AlBERTo Sanfeliu and King-Sun Fu · 1983
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Nltk: The natural language toolkit
Edward Loper and Steven Bird · 2002
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Part-of-speech tagging
Atro Voutilainen · 2003
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Approximate graph edit distance computation by means of bipartite graph matching
Kaspar Riesen and Horst Bunke · 2009
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Part-of-speech tagging from 97% to 100%: is it time for some linguistics?
Christopher D Manning · 2011
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Squad: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang · 2016
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A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel R Bowman · 2017
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graph2vec: Learning distributed representations of graphs
Annamalai Narayanan, Mahinthan Chandramohan, Rajasekar Venkatesan, Lihui Chen, Yang Liu, and Shantanu Jaiswal · 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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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
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Deep contextualized word representations
Matthew Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer · 2018
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Language models learn POS first
Naomi Saphra and Adam Lopez · 2018
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DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf · 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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Language models as knowledge bases?
Fabio Petroni, Tim Rocktäschel, Patrick Lewis, Anton Bakhtin, Yuxiang Wu, Alexander H Miller, and Sebastian Riedel · 2019
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A structural probe for finding syntax in word representations
John Hewitt and Christopher D. Manning · 2019
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Kevin Clark, Urvashi Khandelwal, Omer Levy, and Christopher D. Manning · 2019
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Yoav Goldberg · 2019
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Ian Tenney, Dipanjan Das, and Ellie Pavlick · 2019
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Cheng-Han Chiang, Sung-Feng Huang, and Hung yi Lee · 2020
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Marco Tulio Ribeiro, Tongshuang Wu, Carlos Guestrin, and Sameer Singh · 2020
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How context affects language models’ factual predictions
Fabio Petroni, Patrick Lewis, Aleksandra Piktus, Tim Rocktäschel, Yuxiang Wu, Alexander H. Miller, and Sebastian Riedel · 2020
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How can we know what language models know?
Zhengbao Jiang, Frank F Xu, Jun Araki, and Graham Neubig · 2020
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Unsupervised relation extraction from language models using constrained cloze completion, 2020
Ankur Goswami, Akshata Bhat, Hadar Ohana, and Theodoros Rekatsinas · 2020
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spaCy: Industrial-strength Natural Language Processing in Python, 2020
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Visualizing and understanding the effectiveness of BERT, 2019
Yaru Hao, Li Dong, Furu Wei, and Ke Xu · 2019
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Probing what different NLP tasks teach machines about function word comprehension
Najoung Kim, Roma Patel, Adam Poliak, Patrick Xia, Alex Wang, Tom McCoy, Ian Tenney, Alexis Ross, Tal Linzen, Benjamin Van Durme, Samuel R. Bowman, and Ellie Pavlick · 2019
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Interpretable multi-dataset evaluation for named entity recognition
Jinlan Fu, Pengfei Liu, and Graham Neubig · 2020
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Learning the difference that makes a difference with counterfactually-augmented data
Divyansh Kaushik, Eduard Hovy, and Zachary Lipton · 2020
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Evaluating models’ local decision boundaries via contrast sets, 2020
Matt Gardner, Yoav Artzi, Victoria Basmova, Jonathan Berant, Ben Bogin, Sihao Chen, Pradeep Dasigi, Dheeru Dua, Yanai Elazar, Ananth Gottumukkala, Nitish Gupta, Hanna Hajishirzi, Gabriel Ilharco, Daniel Khashabi, Kevin Lin, Jiangming Liu, Nelson F. Liu, Phoebe Mulcaire, Qiang Ning, Sameer Singh, Noah A. Smith, Sanjay Subramanian, Reut Tsarfaty, Eric Wallace, Ally Zhang, and Ben Zhou · 2020
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Blimp: The benchmark of linguistic minimal pairs for english
Alex Warstadt, Alicia Parrish, Haokun Liu, Anhad Mohananey, Wei Peng, Sheng-Fu Wang, and Samuel R. Bowman · 2020
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Matthew Honnibal, Ines Montani, Sofie Van Landeghem, and Adriane Boyd · 2020
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Characteristic functions on graphs: Birds of a feather, from statistical descriptors to parametric models
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ML and NLP research highlights of 2020
Sebastian Ruder · 2021
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Probing across time: What does roBERTa know and when?, 2021
Leo Z. Liu, Yizhong Wang, Jungo Kasai, Hannaneh Hajishirzi, and Noah A. Smith · 2021
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Tracking the progress of language models by extracting their underlying knowledge graphs, 2021
Carlos Aspillaga, Marcelo Mendoza, and Alvaro Soto · 2021
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https://pypi.org/project/textacy/
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https://ai.googleblog.com/2013/04/50000-lessons-on-how-to-read-relation.html
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