Fetching the paper…
Reading the bibliography…
Effective representation learning from text has been an active area of research in the fields of NLP and text mining.
Stochastic estimation of the maximum of a regression function
Jack Kiefer, Jacob Wolfowitz, et al · 1952
Earlier work this paper cites.
Long Short-Term Memory
Sepp Hochreiter and Jürgen Schmidhuber. 1997 · 1997
Earlier work this paper cites.
Learning word vectors for sentiment analysis. In Proceedings of the 49th annual meeting of the association for computational linguistics: Human language technologies-volume 1 . Association for Computational Linguistics, 142–150
Andrew L Maas, Raymond E Daly, Peter T Pham, Dan Huang, Andrew Y Ng, and Christopher Potts. 2011 · 2011
Earlier work this paper cites.
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.
Neural Machine Translation by Jointly Learning to Align and Translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014 · 2014
Earlier work this paper cites.
Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) . Association for Computational Linguistics, Doha, Qatar, 1724–1734
Kyunghyun Cho, Bart van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014 · 2014
Earlier work this paper cites.
Empirical evaluation of gated recurrent neural networks on sequence modeling. In NIPS 2014 Workshop on Deep Learning, December 2014
Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio. 2014 · 2014
Earlier work this paper cites.
Convolutional Neural Networks for Sentence Classification. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) . Association for Computational Linguistics, Doha, Qatar, 1746–1751
Yoon Kim. 2014 · 2014
Earlier work this paper cites.
Teaching machines to read and comprehend. In Advances in neural information processing systems . 1693–1701
Karl Moritz Hermann, Tomas Kocisky, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom. 2015 · 2015
Earlier work this paper cites.
Diederik P. Kingma and Jimmy Ba. 2015 · 2015
Earlier work this paper cites.
Bilinear cnn models for fine-grained visual recognition. In Proceedings of the IEEE International Conference on Computer Vision . 1449–1457
Tsung-Yu Lin, Aruni RoyChowdhury, and Subhransu Maji. 2015 · 2015
Earlier work this paper cites.
Effective Approaches to Attention-based Neural Machine Translation. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing . Association for Computational Linguistics, Lisbon, Portugal, 1412–1421
Thang Luong, Hieu Pham, and Christopher D. Manning. 2015 · 2015
Earlier work this paper cites.
Combining word embeddings and feature embeddings for fine-grained relation extraction. In Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies . 1374–1379
Mo Yu, Matthew R Gormley, and Mark Dredze. 2015 · 2015
Earlier work this paper cites.
Character-level convolutional networks for text classification. In Advances in neural information processing systems . 649–657
Xiang Zhang, Junbo Zhao, and Yann LeCun. 2015 · 2015
Earlier work this paper cites.
Long Short-Term Memory-Networks for Machine Reading. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing . Association for Computational Linguistics, Austin, Texas, 551–561
Jianpeng Cheng, Li Dong, and Mirella Lapata. 2016 · 2016
Earlier work this paper cites.
A decomposable attention model for natural language inference
Ankur P Parikh, Oscar Täckström, Dipanjan Das, and Jakob Uszkoreit. 2016 · 2016
Cited alongside, same era.
Convolutional neural networks with low-rank regularization
Cheng Tai, Tong Xiao, Yi Zhang, Xiaogang Wang, and E. Weinan. 2016 · 2016
Cited alongside, same era.
A Simple but Tough-to-Beat Baseline for Sentence Embeddings
Sanjeev Arora, Yingyu Liang, and Tengyu Ma. 2017 · 2017
Cited alongside, same era.
Reading Wikipedia to Answer Open-Domain Questions. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) . Association for Computational Linguistics, Vancouver, Canada, 1870–1879
Danqi Chen, Adam Fisch, Jason Weston, and Antoine Bordes. 2017 · 2017
Cited alongside, same era.
UCI Machine Learning Repository
Dua Dheeru and Efi Karra Taniskidou. 2017 · 2017
Cited alongside, same era.
Attention is all you need. In Advances in neural information processing systems . 5998–6008
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Later among the works it cites.
Multi-modal factorized bilinear pooling with co-attention learning for visual question answering. In Proceedings of the IEEE international conference on computer vision . 1821–1830
Zhou Yu, Jun Yu, Jianping Fan, and Dacheng Tao. 2017 · 2017
Later among the works it cites.
Adaptive mixture of low-rank factorizations for compact neural modeling
Ting Chen, Ji Lin, Tian Lin, Song Han, Chong Wang, and Denny Zhou. 2018 · 2018
Later among the works it cites.
Universal Language Model Fine-tuning for Text Classification. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) . Association for Computational Linguistics, Melbourne, Australia, 328–339
Jeremy Howard and Sebastian Ruder. 2018 · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Gated-Attention Readers for Text Comprehension. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) . Association for Computational Linguistics, Vancouver, Canada, 1832–1846
Bhuwan Dhingra, Hanxiao Liu, Zhilin Yang, William Cohen, and Ruslan Salakhutdinov. 2017 · 2017
Cited alongside, same era.
Convolutional Sequence to Sequence Learning. In Proceedings of the 34th International Conference on Machine Learning - Volume 70 (ICML’17) . JMLR.org, 1243–1252
Jonas Gehring, Michael Auli, David Grangier, Denis Yarats, and Yann N. Dauphin. 2017 · 2017
Cited alongside, same era.
End-to-end multi-view networks for text classification
Hongyu Guo, Colin Cherry, and Jiang Su. 2017 · 2017
Cited alongside, same era.
Hadamard Product for Low-rank Bilinear Pooling. In ICLR . OpenReview.net
Jin-Hwa Kim et al · 2017
Cited alongside, same era.
Factorization tricks for LSTM networks
Oleksii Kuchaiev and Boris Ginsburg. 2017 · 2017
Cited alongside, same era.
A Structured Self-Attentive Sentence Embedding
Zhouhan Lin, Minwei Feng, Cicero Nogueira dos Santos, Mo Yu, Bing Xiang, Bowen Zhou, and Yoshua Bengio. 2017 · 2017
Cited alongside, same era.
A deep reinforced model for abstractive summarization
Romain Paulus, Caiming Xiong, and Richard Socher. 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: Human Language Technologies, Volume 1 (Long Papers) . Association for Computational Linguistics, New Orleans, Louisiana, 2227–2237
Matthew Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
Later among the works it cites.
Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) . Association for Computational Linguistics, Melbourne, Australia, 440–450
Dinghan Shen, Guoyin Wang, Wenlin Wang, Martin Renqiang Min, Qinliang Su, Yizhe Zhang, Chunyuan Li, Ricardo Henao, and Lawrence Carin. 2018 · 2018
Later among the works it cites.
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: Human Language Technologies, Volume 1 (Long and Short Papers) . Association for Computational Linguistics, Minneapolis, Minnesota, 4171–4186
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Closest in time.
Albert: A lite bert for self-supervised learning of language representations
Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Soricut. 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.
Event Detection Using Hierarchical Multi-Aspect Attention. In The World Wide Web Conference (WWW ’19) . ACM, New York, NY, USA, 3079–3085
Sneha Mehta, Mohammad Raihanul Islam, Huzefa Rangwala, and Naren Ramakrishnan. 2019 · 2019
Closest in time.
Are Sixteen Heads Really Better than One?. In Advances in Neural Information Processing Systems . 14014–14024
Paul Michel, Omer Levy, and Graham Neubig. 2019 · 2019
Closest in time.
Transformers: State-of-the-art Natural Language Processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, et al · 2019
Closest in time.
Xlnet: Generalized autoregressive pretraining for language understanding. In Advances in neural information processing systems . 5754–5764
Zhilin Yang et al · 2019
Closest in time.
VL-BERT: Pre-training of Generic Visual-Linguistic Representations. In International Conference on Learning Representations
Weijie Su, Xizhou Zhu, Yue Cao, Bin Li, Lewei Lu, Furu Wei, and Jifeng Dai. 2020 · 2020
Closest in time.