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We consider the extreme multi-label text classification (XMC) problem: given an input text, return the most relevant labels from a large label collection.
Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems . 3111–3119
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013 · 2013
Earlier work this paper cites.
Adam: A method for stochastic optimization. In Proceedings of the International Conference on Learning Representations
Diederik Kingma and Jimmy Ba. 2014 · 2014
Earlier work this paper cites.
Balanced k-means for clustering. In Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR) . Springer, 32–41
Mikko I Malinen and Pasi Fränti. 2014 · 2014
Earlier work this paper cites.
Glove: Global vectors for word representation. In EMNLP . 1532–1543
Jeffrey Pennington, Richard Socher, and Christopher D Manning. 2014 · 2014
Earlier work this paper cites.
Fastxml: A fast, accurate and stable tree-classifier for extreme multi-label learning. In KDD
Yashoteja Prabhu and Manik Varma. 2014 · 2014
Earlier work this paper cites.
Sparse local embeddings for extreme multi-label classification. In NIPS
Kush Bhatia, Himanshu Jain, Purushottam Kar, Manik Varma, and Prateek Jain. 2015 · 2015
Earlier work this paper cites.
LSHTC: A benchmark for large-scale text classification
Ioannis Partalas, Aris Kosmopoulos, Nicolas Baskiotis, Thierry Artieres, George Paliouras, Eric Gaussier, Ion Androutsopoulos, Massih-Reza Amini, and Patrick Galinari. 2015 · 2015
Earlier work this paper cites.
Extreme multi-label loss functions for recommendation, tagging, ranking & other missing label applications. In KDD
Himanshu Jain, Yashoteja Prabhu, and Manik Varma. 2016 · 2016
Earlier work this paper cites.
DiSMEC: distributed sparse machines for extreme multi-label classification. In WSDM
Rohit Babbar and Bernhard Schölkopf. 2017 · 2017
Earlier work this paper cites.
Deep learning for extreme multi-label text classification. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval . ACM, 115–124
Jingzhou Liu, Wei-Cheng Chang, Yuexin Wu, and Yiming Yang. 2017 · 2017
Earlier work this paper cites.
Maximizing Subset Accuracy with Recurrent Neural Networks in Multi-label Classification. In NIPS
Jinseok Nam, Eneldo Loza Mencía, Hyunwoo J Kim, and Johannes Fürnkranz. 2017 · 2017
Earlier work this paper cites.
AnnexML: Approximate nearest neighbor search for extreme multi-label classification. In Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining . 455–464
Yukihiro Tagami. 2017 · 2017
Cited alongside, same era.
Attention is all you need. In NIPS
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Cited alongside, same era.
PPDsparse: A parallel primal-dual sparse method for extreme classification. In KDD . ACM
Ian EH Yen, Xiangru Huang, Wei Dai, Pradeep Ravikumar, Inderjit Dhillon, and Eric Xing. 2017 · 2017
Cited alongside, same era.
Deep contextualized word representations. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)
Matthew E Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
Cited alongside, same era.
Parabel: Partitioned label trees for extreme classification with application to dynamic search advertising. In WWW
Slice: Scalable Linear Extreme Classifiers Trained on 100 Million Labels for Related Searches. In Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining . ACM, 528–536
Himanshu Jain, Venkatesh Balasubramanian, Bhanu Chunduri, and Manik Varma. 2019 · 2019
Closest in time.
Bonsai-Diverse and Shallow Trees for Extreme Multi-label Classification
Sujay Khandagale, Han Xiao, and Rohit Babbar. 2019 · 2019
Closest in time.
Latent retrieval for weakly supervised open domain question answering. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL)
Kenton Lee, Ming-Wei Chang, and Kristina Toutanova. 2019 · 2019
Closest in time.
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 · 2019
Closest in time.
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Yashoteja Prabhu, Anil Kag, Shrutendra Harsola, Rahul Agrawal, and Manik Varma. 2018 · 2018
Cited alongside, same era.
Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. 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 R Bowman. 2018 · 2018
Cited alongside, same era.
A no-regret generalization of hierarchical softmax to extreme multi-label classification. In NIPS
Marek Wydmuch, Kalina Jasinska, Mikhail Kuznetsov, Róbert Busa-Fekete, and Krzysztof Dembczynski. 2018 · 2018
Cited alongside, same era.
Data scarcity, robustness and extreme multi-label classification
Rohit Babbar and Bernhard Schölkopf. 2019 · 2019
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Cited alongside, same era.
Breaking the Glass Ceiling for Embedding-Based Classifiers for Large Output Spaces. In Advances in Neural Information Processing Systems . 4944–4954
Chuan Guo, Ali Mousavi, Xiang Wu, Daniel N Holtmann-Rice, Satyen Kale, Sashank Reddi, and Sanjiv Kumar. 2019 · 2019
Cited alongside, same era.
Stochastic Negative Mining for Learning with Large Output Spaces. In AISTATS
Sashank J Reddi, Satyen Kale, Felix Yu, Dan Holtmann-Rice, Jiecao Chen, and Sanjiv Kumar. 2019 · 2019
Closest in time.
The Extreme Classification Repository: Multi-label Datasets & Code
Manik Varma. 2019 · 2019
Closest in time.
HuggingFace’s Transformers: State-of-the-art Natural Language Processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, R’emi Louf, Morgan Funtowicz, and Jamie Brew. 2019 · 2019
Closest in time.
XLNet: Generalized Autoregressive Pretraining for Language Understanding. In NIPS
Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, and Quoc V Le. 2019 · 2019
Closest in time.
AttentionXML: Label Tree-based Attention-Aware Deep Model for High-Performance Extreme Multi-Label Text Classification. In Advances in Neural Information Processing Systems . 5812–5822
Ronghui You, Zihan Zhang, Ziye Wang, Suyang Dai, Hiroshi Mamitsuka, and Shanfeng Zhu. 2019 · 2019
Closest in time.
Pre-training Tasks for Embedding-based Large-scale Retrieval. In International Conference on Learning Representations
Wei-Cheng Chang, Felix X. Yu, Yin-Wen Chang, Yiming Yang, and Sanjiv Kumar. 2020 · 2020
Closest in time.