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Similarity measures are a vital tool for understanding how language models represent and process language.
Contextual correlates of synonymy
Herbert Rubenstein and John B. Goodenough. 1965 · 1965
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A study on similarity and relatedness using distributional and WordNet-based approaches
Eneko Agirre, Enrique Alfonseca, Keith Hall, Jana Kravalova, Marius Paşca, and Aitor Soroa. 2009 · 2009
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Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013 · 2013
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Don’t count, predict! a systematic comparison of context-counting vs. context-predicting semantic vectors
Marco Baroni, Georgiana Dinu, and Germán Kruszewski. 2014 · 2014
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GloVe: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014 · 2014
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On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
Sebastian Bach, Alexander Binder, Grégoire Montavon, Frederick Klauschen, Klaus-Robert Müller, and Wojciech Samek. 2015 · 2015
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SimLex-999: Evaluating semantic models with (genuine) similarity estimation
Felix Hill, Roi Reichart, and Anna Korhonen. 2015 · 2015
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Evaluation methods for unsupervised word embeddings
Tobias Schnabel, Igor Labutov, David Mimno, and Thorsten Joachims. 2015 · 2015
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SimVerb-3500: A large-scale evaluation set of verb similarity
Daniela Gerz, Ivan Vulić, Felix Hill, Roi Reichart, and Anna Korhonen. 2016 · 2016
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Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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On the importance of single directions for generalization
Ari S. Morcos, David G.T. Barrett, Neil C. Rabinowitz, and Matthew Botvinick. 2018 · 2018
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All-but-the-top: Simple and effective postprocessing for word representations
Jiaqi Mu and Pramod Viswanath. 2018 · 2018
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Blackbox meets blackbox: Representational similarity & stability analysis of neural language models and brains
Samira Abnar, Lisa Beinborn, Rochelle Choenni, and Willem Zuidema. 2019 · 2019
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Correlating neural and symbolic representations of language
Grzegorz Chrupała and Afra Alishahi. 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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How contextual are contextualized word representations? Comparing the geometry of BERT, ELMo, and GPT-2 embeddings
Kawin Ethayarajh. 2019 · 2019
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Representation degeneration problem in training natural language generation models
Jun Gao, Di He, Xu Tan, Tao Qin, Liwei Wang, and Tieyan Liu. 2019 · 2019
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Linguistic knowledge and transferability of contextual representations
Nelson F. Liu, Matt Gardner, Yonatan Belinkov, Matthew E. Peters, and Noah A. Smith. 2019 · 2019
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On measuring social biases in sentence encoders
Chandler May, Alex Wang, Shikha Bordia, Samuel R. Bowman, and Rachel Rudinger. 2019 · 2019
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Language models are unsupervised multitask learners
A. Radford, Jeffrey Wu, R. Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
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Visualizing and measuring the geometry of bert
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. 2020 · 2020
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Leveraging contextual embeddings for detecting diachronic semantic shift
Matej Martinc, Petra Kralj Novak, and Senja Pollak. 2020 · 2020
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What happens to BERT embeddings during fine-tuning?
Amil Merchant, Elahe Rahimtoroghi, Ellie Pavlick, and Ian Tenney. 2020 · 2020
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What do you mean, BERT?
Timothee Mickus, Denis Paperno, Mathieu Constant, and Kees van Deemter. 2020 · 2020
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Analyzing the source and target contributions to predictions in neural machine translation
Elena Voita, Rico Sennrich, and Ivan Titov. 2020 · 2020
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Emily Reif, Ann Yuan, Martin Wattenberg, Fernanda B Viegas, Andy Coenen, Adam Pearce, and Been Kim. 2019 · 2019
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Evaluating word embedding models: methods and experimental results
Bin Wang, Angela Wang, Fenxiao Chen, Yuncheng Wang, and C.-C. Jay Kuo. 2019 · 2019
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Xlnet: Generalized autoregressive pretraining for language understanding
Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Russ R Salakhutdinov, and Quoc V Le. 2019 · 2019
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Correlation coefficients and semantic textual similarity
Vitalii Zhelezniak, Aleksandar Savkov, April Shen, and Nils Hammerla. 2019 · 2019
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Word associations and the distance properties of context-aware word embeddings
Maria A. Rodriguez and Paola Merlo. 2020 · 2020
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Interpreting Pretrained Contextualized Representations via Reductions to Static Embeddings
Rishi Bommasani, Kelly Davis, and Claire Cardie. 2020 · 2020
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When is a bishop not like a rook? when it’s like a rabbi! multi-prototype BERT embeddings for estimating semantic relationships
Gabriella Chronis and Katrin Erk. 2020 · 2020
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Ivan Vulić, Edoardo Maria Ponti, Robert Litschko, Goran Glavaš, and Anna Korhonen. 2020 · 2020
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Bertscore: Evaluating text generation with bert
Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q. Weinberger, and Yoav Artzi. 2020 · 2020
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Too much in common: Shifting of embeddings in transformer language models and its implications
Daniel Biś, Maksim Podkorytov, and Xiuwen Liu. 2021 · 2021
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Isotropy in the contextual embedding space: Clusters and manifolds
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Probing for idiomaticity in vector space models
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Positional artefacts propagate through masked language model embeddings
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