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Learning sentence embeddings often requires a large amount of labeled data.
Okapi at TREC-3
Stephen E. Robertson, Steve Walker, Susan Jones, Micheline Hancock-Beaulieu, and Mike Gatford. 1994 · 1994
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Dimensionality reduction by learning an invariant mapping
Raia Hadsell, Sumit Chopra, and Yann LeCun. 2006 · 2006
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Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion
Pascal Vincent, Hugo Larochelle, Isabelle Lajoie, Yoshua Bengio, and Pierre-Antoine Manzagol. 2010 · 2010
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The Stanford CoreNLP natural language processing toolkit
Christopher Manning, Mihai Surdeanu, John Bauer, Jenny Finkel, Steven Bethard, and David McClosky. 2014 · 2014
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014 · 2014
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Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015 · 2015
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Doris Hoogeveen, Karin M. Verspoor, and Timothy Baldwin. 2015 · 2015
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Wei Xu, Chris Callison-Burch, and Bill Dolan. 2015 · 2015
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Deep Learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville. 2016 · 2016
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Learning distributed representations of sentences from unlabelled data
Felix Hill, Kyunghyun Cho, and Anna Korhonen. 2016 · 2016
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Tao Lei, Hrishikesh Joshi, Regina Barzilay, Tommi S. Jaakkola, Kateryna Tymoshenko, Alessandro Moschitti, and Lluís Màrquez. 2016 · 2016
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Task-oriented intrinsic evaluation of semantic textual similarity
Nils Reimers, Philip Beyer, and Iryna Gurevych. 2016 · 2016
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SemEval-2017 task 1: Semantic textual similarity multilingual and crosslingual focused evaluation
Daniel Cer, Mona Diab, Eneko Agirre, Iñigo Lopez-Gazpio, and Lucia Specia. 2017 · 2017
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Supervised learning of universal sentence representations from natural language inference data
Alexis Conneau, Douwe Kiela, Holger Schwenk, Loïc Barrault, and Antoine Bordes. 2017 · 2017
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Efficient Natural Language Response Suggestion for Smart Reply
Matthew L. Henderson, Rami Al-Rfou, Brian Strope, Yun-Hsuan Sung, László Lukács, Ruiqi Guo, Sanjiv Kumar, Balint Miklos, and Ray Kurzweil. 2017 · 2017
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Wuwei Lan, Siyu Qiu, Hua He, and Wei Xu. 2017 · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Sentence-bert: Sentence embeddings using siamese bert-networks
Nils Reimers and Iryna Gurevych. 2019 · 2019
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Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey E. Hinton. 2020 · 2020
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SPECTER: document-level representation learning using citation-informed transformers
Arman Cohan, Sergey Feldman, Iz Beltagy, Doug Downey, and Daniel S. Weld. 2020 · 2020
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BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. 2020 · 2020
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Bohan Li, Hao Zhou, Junxian He, Mingxuan Wang, Yiming Yang, and Lei Li. 2020 · 2020
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Universal sentence encoder for english
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Unsupervised learning of sentence embeddings using compositional n-gram features
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A broad-coverage challenge corpus for sentence understanding through inference
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Learning universal sentence representations with mean-max attention autoencoder
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Exploring the limits of transfer learning with a unified text-to-text transformer
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Multilingual universal sentence encoder for semantic retrieval
Yinfei Yang, Daniel Cer, Amin Ahmad, Mandy Guo, Jax Law, Noah Constant, Gustavo Hernández Ábrego, Steve Yuan, Chris Tar, Yun-Hsuan Sung, Brian Strope, and Ray Kurzweil. 2020 · 2020
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Semantic re-tuning with contrastive tension
Fredrik Carlsson, Magnus Sahlgren, Evangelia Gogoulou, Amaru Cuba Gyllensten, and Erik Ylipää Hellqvist. 2021 · 2021
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