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Extreme Multi-label text Classification (XMC) is a task of finding the most relevant labels from a large label set.
Bonsai–Diverse and Shallow Trees for Extreme Multi-label Classification
Khandagale, S.; Xiao, H.; and Babbar, R. 2019 · 1904
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Roberta: A robustly optimized bert pretraining approach
Liu, Y.; Ott, M.; Goyal, N.; Du, J.; Joshi, M.; Chen, D.; Levy, O.; Lewis, M.; Zettlemoyer, L.; and Stoyanov, V. 2019 · 1907
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Efficient pairwise multilabel classification for large-scale problems in the legal domain
Mencia, E. L.; and Fürnkranz, J. 2008 · 2008
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Multiclass-multilabel classification with more classes than examples
Dekel, O.; and Shamir, O. 2010 · 2010
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Enhancing navigation on wikipedia with social tags
Zubiaga, A. 2012 · 2012
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Hidden factors and hidden topics: understanding rating dimensions with review text
McAuley, J.; and Leskovec, J. 2013 · 2013
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Adam: A method for stochastic optimization
Kingma, D. P.; and Ba, J. 2014 · 2014
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Fastxml: A fast, accurate and stable tree-classifier for extreme multi-label learning
Prabhu, Y.; and Varma, M. 2014 · 2014
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Sparse local embeddings for extreme multi-label classification
Bhatia, K.; Jain, H.; Kar, P.; Varma, M.; and Jain, P. 2015 · 2015
Cited alongside, same era.
Pd-sparse: A primal and dual sparse approach to extreme multiclass and multilabel classification
Yen, I. E.-H.; Huang, X.; Ravikumar, P.; Zhong, K.; and Dhillon, I. 2016 · 2016
Cited alongside, same era.
Dismec: Distributed sparse machines for extreme multi-label classification
Babbar, R.; and Schölkopf, B. 2017 · 2017
Cited alongside, same era.
Deep learning for extreme multi-label text classification
Liu, J.; Chang, W.-C.; Wu, Y.; and Yang, Y. 2017 · 2017
Cited alongside, same era.
Annexml: Approximate nearest neighbor search for extreme multi-label classification
Tagami, Y. 2017 · 2017
Cited alongside, same era.
Attention is all you need
Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, Ł.; and Polosukhin, I. 2017 · 2017
Averaging weights leads to wider optima and better generalization
Izmailov, P.; Podoprikhin, D.; Garipov, T.; Vetrov, D.; and Wilson, A. G. 2018 · 2018
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Parabel: Partitioned label trees for extreme classification with application to dynamic search advertising
Prabhu, Y.; Kag, A.; Harsola, S.; Agrawal, R.; and Varma, M. 2018 · 2018
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A no-regret generalization of hierarchical softmax to extreme multi-label classification
Wydmuch, M.; Jasinska, K.; Kuznetsov, M.; Busa-Fekete, R.; and Dembczynski, K. 2018 · 2018
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Data scarcity, robustness and extreme multi-label classification
Babbar, R.; and Schölkopf, B. 2019 · 2019
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Xlnet: Generalized autoregressive pretraining for language understanding
Yang, Z.; Dai, Z.; Yang, Y.; Carbonell, J.; Salakhutdinov, R. R.; and Le, Q. V. 2019 · 2019
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Cited alongside, same era.
Ppdsparse: A parallel primal-dual sparse method for extreme classification
Yen, I. E.; Huang, X.; Dai, W.; Ravikumar, P.; Dhillon, I.; and Xing, E. 2017 · 2017
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J.; Chang, M.-W.; Lee, K.; and Toutanova, K. 2018 · 2018
Cited alongside, same era.
Attentionxml: Label tree-based attention-aware deep model for high-performance extreme multi-label text classification
You, R.; Zhang, Z.; Wang, Z.; Dai, S.; Mamitsuka, H.; and Zhu, S. 2019 · 2019
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Taming Pretrained Transformers for Extreme Multi-label Text Classification
Chang, W.-C.; Yu, H.-F.; Zhong, K.; Yang, Y.; and Dhillon, I. S. 2020 · 2020
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