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The impressive achievements of transformers force NLP researchers to delve into how these models represent the underlying structure of natural language.
Assessing bert’s syntactic abilities
Yoav Goldberg. 2019 · 1901
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Adaptation of deep bidirectional multilingual transformers for russian language
Yuri Kuratov and Mikhail Y. Arkhipov. 2019 · 1905
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Visualizing and measuring the geometry of BERT
Andy Coenen, Emily Reif, Ann Yuan, Been Kim, Adam Pearce, Fernanda B. Viégas, and Martin Wattenberg. 2019 · 1906
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The origins of the gini index: extracts from variabilità e mutabilità (1912) by corrado gini
Lidia Ceriani and Paolo Verme. 2012 · 1912
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Wietse de Vries, Andreas van Cranenburgh, Arianna Bisazza, Tommaso Caselli, Gertjan van Noord, and Malvina Nissim. 2019 · 1912
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Language Universals and Linguistic Typology: Syntax and Morphology
B. Comrie. 1989 · 1989
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A Systematic Analysis of Morphological Content in BERT Models for Multiple Languages
Daniel Edmiston. 2020 · 2004
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Canonical correlation analysis: An overview with application to learning methods
David R Hardoon, Sandor Szedmak, and John Shawe-Taylor. 2004 · 2004
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Parsbert: Transformer-based model for persian language understanding
Mehrdad Farahani, Mohammad Gharachorloo, Marzieh Farahani, and Mohammad Manthouri. 2020 · 2005
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Language Universals
Joseph H. Greenberg. 2005 · 2005
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Europarl: A parallel corpus for statistical machine translation
Philipp Koehn. 2005 · 2005
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The Oxford handbook of linguistic typology
Jae Jung Song. 2010 · 2010
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Word order
Jae Jung Song. 2012 · 2012
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WALS Online
Matthew S. Dryer and Martin Haspelmath, editors. 2013 · 2013
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Historical linguistics: an introduction
Winfred P Lehmann. 2013 · 2013
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Language phylogenies
Michael Dunn. 2015 · 2015
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The language capacity: architecture and evolution
Noam Chomsky. 2017 · 2017
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URIEL and lang2vec: Representing languages as typological, geographical, and phylogenetic vectors
Patrick Littell, David R. Mortensen, Ke Lin, Katherine Kairis, Carlisle Turner, and Lori Levin. 2017 · 2017
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SVCCA: Singular Vector Canonical Correlation Analysis for Deep Learning Dynamics and Interpretability
Maithra Raghu, Justin Gilmer, Jason Yosinski, and Jascha Sohl-Dickstein. 2017 · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Insights on representational similarity in neural networks with canonical correlation
Multilingual is not enough: Bert for finnish
Antti Virtanen, Jenna Kanerva, Rami Ilo, Jouni Luoma, Juhani Luotolahti, Tapio Salakoski, Filip Ginter, and Sampo Pyysalo. 2019 · 2019
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Spanish pre-trained bert model and evaluation data
José Cañete, Gabriel Chaperon, Rodrigo Fuentes, Jou-Hui Ho, Hojin Kang, and Jorge Pérez. 2020 · 2020
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Finding universal grammatical relations in multilingual BERT
Ethan A. Chi, John Hewitt, and Christopher D. Manning. 2020 · 2020
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The birth of Romanian BERT
Stefan Dumitrescu, Andrei-Marius Avram, and Sampo Pyysalo. 2020 · 2020
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Greek-bert: The greeks visiting sesame street
John Koutsikakis, Ilias Chalkidis, Prodromos Malakasiotis, and Ion Androutsopoulos. 2020 · 2020
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Playing with words at the national library of sweden – making a swedish bert
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Ari Morcos, Maithra Raghu, and Samy Bengio. 2018 · 2018
Cited alongside, same era.
German bert
Deepset. 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
Cited alongside, same era.
A structural probe for finding syntax in word representations
John Hewitt and Christopher D. Manning. 2019 · 2019
Cited alongside, same era.
What does BERT learn about the structure of language?
Ganesh Jawahar, Benoît Sagot, and Djamé Seddah. 2019 · 2019
Cited alongside, same era.
Similarity of neural network representations revisited
Simon Kornblith, Mohammad Norouzi, Honglak Lee, and Geoffrey Hinton. 2019 · 2019
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Multilingual probing of deep pre-trained contextual encoders
Vinit Ravishankar, Memduh Gökırmak, Lilja Øvrelid, and Erik Velldal. 2019 · 2019
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Martin Malmsten, Love Börjeson, and Chris Haffenden. 2020 · 2020
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Probing multilingual BERT for genetic and typological signals
Taraka Rama, Lisa Beinborn, and Steffen Eger. 2020 · 2020
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Probing pretrained language models for lexical semantics
Ivan Vulić, Edoardo Maria Ponti, Robert Litschko, Goran Glavaš, and Anna Korhonen. 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, Remi 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 Rush. 2020 · 2020
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KERMIT: Complementing transformer architectures with encoders of explicit syntactic interpretations
Fabio Massimo Zanzotto, Andrea Santilli, Leonardo Ranaldi, Dario Onorati, Pierfrancesco Tommasino, and Francesca Fallucchi. 2020 · 2020
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Lifelong pretraining: Continually adapting language models to emerging corpora
Xisen Jin, Dejiao Zhang, Henghui Zhu, Wei Xiao, Shang-Wen Li, Xiaokai Wei, Andrew Arnold, and Xiang Ren. 2022 · 2022
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Word-order typology in multilingual BERT: A case study in subordinate-clause detection
Dmitry Nikolaev and Sebastian Pado. 2022 · 2022
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Cross-linguistic comparison of linguistic feature encoding in BERT models for typologically different languages
Yulia Otmakhova, Karin Verspoor, and Jey Han Lau. 2022 · 2022
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The dark side of the language: Syntax-based neural networks rivaling transformers in definitely unseen sentences
Dario Onorati, Leonardo Ranaldi, Aria Nourbakhsh, Elena Sofia Ruzzetti, Arianna Patrizi, Francesca Fallucchi, and Fabio Massimo Zanzotto. 2023 · 2023
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