Fetching the paper…
Reading the bibliography…
BERT, as one of the pretrianed language models, attracts the most attention in recent years for creating new benchmarks across GLUE tasks via fine-tuning.
Assessing BERT’s syntactic abilities
Yoav Goldberg. 2019 · 1901
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.
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.
Self-attention attribution: Interpreting information interactions inside transformer
Yaru Hao, Li Dong, Furu Wei, and Ke Xu. 2020 · 2004
Earlier work this paper cites.
Pearson correlation coefficient
Jacob Benesty, Jingdong Chen, Yiteng Huang, and Israel Cohen. 2009 · 2009
Earlier work this paper cites.
Analyzing the source and target contributions to predictions in neural machine translation
Elena Voita, Rico Sennrich, and Ivan Titov. 2020 · 2010
Earlier work this paper cites.
Learning word vectors for sentiment analysis
Andrew Maas, Raymond E Daly, Peter T Pham, Dan Huang, Andrew Y Ng, and Christopher Potts. 2011 · 2011
Earlier work this paper cites.
Recursive deep models for semantic compositionality over a sentiment treebank
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D Manning, Andrew Y Ng, and Christopher Potts. 2013 · 2013
Earlier work this paper cites.
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
Earlier work this paper cites.
Character-level convolutional networks for text classification
Xiang Zhang, Junbo Zhao, and Yann LeCun. 2015 · 2015
Earlier work this paper cites.
Gaussian error linear units (gelus)
Dan Hendrycks and Kevin Gimpel. 2016 · 2016
Cited alongside, same era.
Visualizing and understanding neural models in NLP
Jiwei Li, Xinlei Chen, Eduard Hovy, and Dan Jurafsky. 2016 · 2016
Cited alongside, same era.
Explaining recurrent neural network predictions in sentiment analysis
Leila Arras, Grégoire Montavon, Klaus-Robert Müller, and Wojciech Samek. 2017 · 2017
Cited alongside, same era.
The (un) reliability of saliency methods
Pieter-Jan Kindermans, Sara Hooker, Julius Adebayo, Maximilian Alber, Kristof T Schütt, Sven Dähne, Dumitru Erhan, and Been Kim. 2017 · 2017
Cited alongside, same era.
SemEval-2017 task 4: Sentiment analysis in Twitter
Sara Rosenthal, Noura Farra, and Preslav Nakov. 2017 · 2017
Cited alongside, same era.
Layer-wise relevance propagation for explaining deep neural network decisions in mri-based alzheimer’s disease classification
Moritz Böhle, Fabian Eitel, Martin Weygandt, and Kerstin Ritter. 2019 · 2019
Later among the works it cites.
What does BERT look at? An analysis of BERT’s attention
Kevin Clark, Urvashi Khandelwal, Omer Levy, and Christopher D Manning. 2019 · 2019
Later among the works it cites.
BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Later among the works it cites.
A structural probe for finding syntax in word representations
John Hewitt and Christopher D Manning. 2019 · 2019
Later among the works it cites.
Bert rediscovers the classical nlp pipeline
Ian Tenney, Dipanjan Das, and Ellie Pavlick. 2019 · 2019
Later among the works it cites.
Analyzing the structure of attention in a transformer language model
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Learning important features through propagating activation differences
Avanti Shrikumar, Peyton Greenside, and Anshul Kundaje. 2017 · 2017
Cited alongside, same era.
An analysis of encoder representations in Transformer-based machine translation
Alessandro Raganato and Jörg Tiedemann. 2018 · 2018
Cited alongside, same era.
Context-aware neural machine translation learns anaphora resolution
Elena Voita, Pavel Serdyukov, Rico Sennrich, and Ivan Titov. 2018 · 2018
Cited alongside, same era.
Glue: A multi-task benchmark and analysis platform for natural language understanding
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel Bowman. 2018 · 2018
Cited alongside, same era.
Explaining therapy predictions with layer-wise relevance propagation in neural networks
Yinchong Yang, Volker Tresp, Marius Wunderle, and Peter A Fasching. 2018 · 2018
Cited alongside, same era.
Jesse Vig and Yonatan Belinkov. 2019 · 2019
Later among the works it cites.
Analyzing multi-head self-attention: Specialized heads do the heavy lifting, the rest can be pruned
Elena Voita, David Talbot, Fedor Moiseev, Rico Sennrich, and Ivan Titov. 2019 · 2019
Later among the works it cites.
Xlnet: Generalized autoregressive pretraining for language understanding
Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Russ R Salakhutdinov, and Quoc V Le. 2019 · 2019
Later among the works it cites.
Quantifying attention flow in transformers
Samira Abnar and Willem Zuidema. 2020 · 2020
Later among the works it cites.
Structured self-AttentionWeights encode semantics in sentiment analysis
Zhengxuan Wu, Thanh-Son Nguyen, and Desmond Ong. 2020 · 2020
Later among the works it cites.