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Large pretrained language models (LMs) have become the central building block of many NLP applications.
Regularization advantages of multilingual neural language models for low resource domains
Navid Rekabsaz, Nikolaos Pappas, James Henderson, Banriskhem K Khonglah, and Srikanth Madikeri. 2019 · 1906
Earlier work this paper cites.
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 · 1907
Earlier work this paper cites.
Cross-lingual name tagging and linking for 282 languages
Xiaoman Pan, Boliang Zhang, Jonathan May, Joel Nothman, Kevin Knight, and Heng Ji. 2017 · 1958
Earlier work this paper cites.
A generalized solution of the orthogonal procrustes problem
Peter H Schönemann. 1966 · 1966
Earlier work this paper cites.
From english to foreign languages: Transferring pre-trained language models
Ke Tran. 2020 · 2002
Earlier work this paper cites.
Electra: Pre-training text encoders as discriminators rather than generators
Kevin Clark, Minh-Thang Luong, Quoc V Le, and Christopher D Manning. 2020 · 2003
Earlier work this paper cites.
What the [mask]? making sense of language-specific bert models
Debora Nozza, Federico Bianchi, and Dirk Hovy. 2020 · 2003
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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 · 2005
Earlier work this paper cites.
Freedict: an open source repository of tei-encoded bilingual dictionaries
Piotr Bański and Beata Wójtowicz. 2009 · 2009
Earlier work this paper cites.
Bilingual word representations with monolingual quality in mind
Thang Luong, Hieu Pham, and Christopher D. Manning. 2015 · 2015
Earlier work this paper cites.
Normalized word embedding and orthogonal transform for bilingual word translation
Chao Xing, Dong Wang, Chao Liu, and Yiye Lin. 2015 · 2015
Earlier work this paper cites.
Learning principled bilingual mappings of word embeddings while preserving monolingual invariance
Mikel Artetxe, Gorka Labaka, and Eneko Agirre. 2016 · 2016
Earlier work this paper cites.
Man is to computer programmer as woman is to homemaker? debiasing word embeddings
Tolga Bolukbasi, Kai-Wei Chang, James Y Zou, Venkatesh Saligrama, and Adam T Kalai. 2016 · 2016
Earlier work this paper cites.
Learning crosslingual word embeddings without bilingual corpora
Long Duong, Hiroshi Kanayama, Tengfei Ma, Steven Bird, and Trevor Cohn. 2016 · 2016
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Transfer learning for low-resource neural machine translation
Barret Zoph, Deniz Yuret, Jonathan May, and Kevin Knight. 2016 · 2016
Earlier work this paper cites.
Learning bilingual word embeddings with (almost) no bilingual data
Mikel Artetxe, Gorka Labaka, and Eneko Agirre. 2017 · 2017
Earlier work this paper cites.
Enriching word vectors with subword information
Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. 2017 · 2017
Earlier work this paper cites.
Semantics derived automatically from language corpora contain human-like biases
Aylin Caliskan, Joanna J Bryson, and Arvind Narayanan. 2017 · 2017
Earlier work this paper cites.
Word translation without parallel data
Alexis Conneau, Guillaume Lample, Marc’Aurelio Ranzato, Ludovic Denoyer, and Hervé Jégou. 2017 · 2017
Cited alongside, same era.
Transfer learning across low-resource, related languages for neural machine translation
Toan Q. Nguyen and David Chiang. 2017 · 2017
Cited alongside, same era.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Cited alongside, same era.
A robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings
Mikel Artetxe, Gorka Labaka, and Eneko Agirre. 2018 · 2018
Cited alongside, same era.
Xnli: Evaluating cross-lingual sentence representations
Alexis Conneau, Ruty Rinott, Guillaume Lample, Adina Williams, Samuel R. Bowman, Holger Schwenk, and Veselin Stoyanov. 2018 · 2018
Cited alongside, same era.
Unsupervised cross-lingual representation learning at scale
Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer, and Veselin Stoyanov. 2020 · 2020
Later among the works it cites.
Pre-training via paraphrasing
Mike Lewis, Marjan Ghazvininejad, Gargi Ghosh, Armen Aghajanyan, Sida Wang, and Luke Zettlemoyer. 2020 · 2020
Later among the works it cites.
BelGPT-2: a GPT-2 model pre-trained on French corpora
Antoine Louis. 2020 · 2020
Later among the works it cites.
CamemBERT: a tasty French language model
Louis Martin, Benjamin Muller, Pedro Javier Ortiz Suárez, Yoann Dupont, Laurent Romary, Éric de la Clergerie, Djamé Seddah, and Benoît Sagot. 2020 · 2020
Later among the works it cites.
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
Later among the works it cites.
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Adversarial removal of demographic attributes from text data
Yanai Elazar and Yoav Goldberg. 2018 · 2018
Cited alongside, same era.
Loss in translation: Learning bilingual word mapping with a retrieval criterion
Armand Joulin, Piotr Bojanowski, Tomas Mikolov, Hervé Jégou, and Edouard Grave. 2018 · 2018
Cited alongside, same era.
Word translation without parallel data
Guillaume Lample, Alexis Conneau, Marc’Aurelio Ranzato, Ludovic Denoyer, and Hervé Jégou. 2018 · 2018
Cited alongside, same era.
Improving language understanding with unsupervised learning
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. 2018 · 2018
Cited alongside, same era.
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.
How multilingual is multilingual BERT?
Telmo Pires, Eva Schlinger, and Dan Garrette. 2019 · 2019
Cited alongside, same era.
Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
Cited alongside, same era.
Are all languages created equal in multilingual BERT?
Shijie Wu and Mark Dredze. 2020 · 2020
Later among the works it cites.
On the dangers of stochastic parrots: Can language models be too big?
Emily M Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell. 2021 · 2021
Closest in time.
As good as new. how to successfully recycle English GPT-2 to make models for other languages
Wietse de Vries and Malvina Nissim. 2021 · 2021
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Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity
William Fedus, Barret Zoph, and Noam Shazeer. 2021 · 2021
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Investigating gender fairness of recommendation algorithms in the music domain
Alessandro B. Melchiorre, Navid Rekabsaz, Emilia Parada-Cabaleiro, Stefan Brandl, Oleg Lesota, and Markus Schedl. 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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Measuring societal biases from text corpora with smoothed first-order co-occurrence
Navid Rekabsaz, Robert West, James Henderson, and Allan Hanbury. 2021b · 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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mT5: A massively multilingual pre-trained text-to-text transformer
Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, and Colin Raffel. 2021 · 2021
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Mitigating consumer biases in recommendations with adversarial training
Christian Ganhör, David Penz, Navid Rekabsaz, Oleg Lesota, and Markus Schedl. 2022 · 2022
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Do perceived gender biases in retrieval results affect relevance judgements?
Klara Krieg, Emilia Parada-Cabaleiro, Markus Schedl, and Navid Rekabsaz. 2022 · 2022
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Mitigating bias in search results through set-based document reranking and neutrality regularization
George Zerveas, Navid Rekabsaz, Daniel Cohen, and Carsten Eickhoff. 2022 · 2022
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Do neural ranking models intensify gender bias?
Navid Rekabsaz and Markus Schedl. 2020 · 2068
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