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Pretrained language models based on the transformer architecture have shown great success in NLP.
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
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A framework for analyzing semantic change of words across time
Adam Jatowt and Kevin Duh. 2014 · 2014
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Temporal analysis of language through neural language models
Yoon Kim, Yi-I Chiu, Kentaro Hanaki, Darshan Hegde, and Slav Petrov. 2014 · 2014
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Statistically significant detection of linguistic change
Vivek Kulkarni, Rami Al-Rfou, Bryan Perozzi, and Steven Skiena. 2015 · 2015
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Diachronic word embeddings reveal statistical laws of semantic change
William L. Hamilton, Jure Leskovec, and Dan Jurafsky. 2016 · 2016
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Temporal information retrieval
Nattiya Kanhabua and Avishek Anand. 2016 · 2016
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Dynamic word embeddings
Robert Bamler and Stephan Mandt. 2017 · 2017
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Learning word relatedness over time
Guy D. Rosin, Eytan Adar, and Kira Radinsky. 2017 · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Diachronic word embeddings and semantic shifts: a survey
Andrey Kutuzov, Lilja Øvrelid, Terrence Szymanski, and Erik Velldal. 2018 · 2018
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Deep neural models of semantic shift
Alex Rosenfeld and Katrin Erk. 2018 · 2018
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Dynamic embeddings for language evolution
Maja R. Rudolph and David M. Blei. 2018 · 2018
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Survey of computational approaches to lexical semantic change
Nina Tahmasebi, Lars Borin, and Adam Jatowt. 2018 · 2018
Cited alongside, same era.
Dynamic word embeddings for evolving semantic discovery
Zijun Yao, Yifan Sun, Weicong Ding, Nikhil Rao, and Hui Xiong. 2018 · 2018
Cited alongside, same era.
What does BERT look at? an analysis of BERT’s attention
Kevin Clark, Urvashi Khandelwal, Omer Levy, and Christopher D. Manning. 2019 · 2019
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.
Time-out: Temporal referencing for robust modeling of lexical semantic change
Haim Dubossarsky, Simon Hengchen, Nina Tahmasebi, and Dominik Schlechtweg. 2019 · 2019
Cited alongside, same era.
Temporal effects on pre-trained models for language processing tasks
Oshin Agarwal and Ani Nenkova. 2021 · 2021
Later among the works it cites.
Dynamic contextualized word embeddings
Valentin Hofmann, Janet Pierrehumbert, and Hinrich Schütze. 2021 · 2021
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Dynamic language models for continuously evolving content
Spurthi Amba Hombaiah, Tao Chen, Mingyang Zhang, Michael Bendersky, and Marc Najork. 2021 · 2021
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Explaining and improving BERT performance on lexical semantic change detection
Severin Laicher, Sinan Kurtyigit, Dominik Schlechtweg, Jonas Kuhn, and Sabine Schulte im Walde. 2021 · 2021
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Mind the gap: Assessing temporal generalization in neural language models
Angeliki Lazaridou, Adhi Kuncoro, Elena Gribovskaya, Devang Agrawal, Adam Liska, Tayfun Terzi, Mai Gimenez, Cyprien de Masson d’Autume, Tomas Kocisky, Sebastian Ruder, et al. 2021 · 2021
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Diachronic sense modeling with deep contextualized word embeddings: An ecological view
Renfen Hu, Shen Li, and Shichen Liang. 2019 · 2019
Cited alongside, same era.
Neural temporality adaptation for document classification: Diachronic word embeddings and domain adaptation models
Xiaolei Huang and Michael J. Paul. 2019 · 2019
Cited alongside, same era.
A wind of change: Detecting and evaluating lexical semantic change across times and domains
Dominik Schlechtweg, Anna Hätty, Marco Del Tredici, and Sabine Schulte im Walde. 2019 · 2019
Cited alongside, same era.
Analysing lexical semantic change with contextualised word representations
Mario Giulianelli, Marco Del Tredici, and Raquel Fernández. 2020 · 2020
Cited alongside, same era.
Simple, interpretable and stable method for detecting words with usage change across corpora
Hila Gonen, Ganesh Jawahar, Djamé Seddah, and Yoav Goldberg. 2020 · 2020
Cited alongside, same era.
Capturing evolution in word usage: Just add more clusters?
Matej Martinc, Syrielle Montariol, Elaine Zosa, and Lidia Pivovarova. 2020b · 2020
Cited alongside, same era.
SemEval-2020 task 1: Unsupervised lexical semantic change detection
Dominik Schlechtweg, Barbara McGillivray, Simon Hengchen, Haim Dubossarsky, and Nina Tahmasebi. 2020 · 2020
Cited alongside, same era.
Scalable and interpretable semantic change detection
Syrielle Montariol, Matej Martinc, and Lidia Pivovarova. 2021 · 2021
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Temporal adaptation of BERT and performance on downstream document classification: Insights from social media
Paul Röttger and Janet Pierrehumbert. 2021 · 2021
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Predicting the age of scientific papers
Pavel Savov, Adam Jatowt, and Radoslaw Nielek. 2021 · 2021
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Time-aware language models as temporal knowledge bases
Bhuwan Dhingra, Jeremy R Cole, Julian Martin Eisenschlos, Daniel Gillick, Jacob Eisenstein, and William W Cohen. 2022 · 2022
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Timelms: Diachronic language models from twitter
Daniel Loureiro, Francesco Barbieri, Leonardo Neves, Luis Espinosa Anke, and Jose Camacho-Collados. 2022 · 2022
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Time masking for temporal language models
Guy D. Rosin, Ido Guy, and Kira Radinsky. 2022 · 2022
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Short-term meaning shift: A distributional exploration
Marco Del Tredici, Raquel Fernández, and Gemma Boleda. 2019 · 2075
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