Fetching the paper…
Reading the bibliography…
We present FewRel 2.0, a more challenging task to investigate two aspects of few-shot relation classification models: (1) Can they adapt to a new domain with only a handful of instances? (2) Can they detect none-of-the-above (NOTA) relations? To construct FewRel 2.0, we build upon the FewRel dataset (Han et al., 2018) by adding a new test set in a quite different domain, and a NOTA relation choice.
Robust classification for imprecise environments
Foster Provost and Tom Fawcett. 2001 · 2001
Earlier work this paper cites.
Domain adaptation with structural correspondence learning
John Blitzer, Ryan McDonald, and Fernando Pereira. 2006 · 2006
Earlier work this paper cites.
Learning to extract relations from the web using minimal supervision
Razvan Bunescu and Raymond Mooney. 2007 · 2007
Earlier work this paper cites.
Semeval-2010 task 8: Multi-way classification of semantic relations between pairs of nominals
Iris Hendrickx, Su Nam Kim, Zornitsa Kozareva, Preslav Nakov, Diarmuid Ó Séaghdha, Sebastian Padó, Marco Pennacchiotti, Lorenza Romano, and Stan Szpakowicz. 2009 · 2009
Earlier work this paper cites.
Distant supervision for relation extraction without labeled data
Mike Mintz, Steven Bills, Rion Snow, and Dan Jurafsky. 2009 · 2009
Earlier work this paper cites.
Domain adaptation via transfer component analysis
Sinno Jialin Pan, Ivor W Tsang, James T Kwok, and Qiang Yang. 2010 · 2010
Earlier work this paper cites.
Unsupervised domain adaptation by domain invariant projection
Mahsa Baktashmotlagh, Mehrtash T Harandi, Brian C Lovell, and Mathieu Salzmann. 2013 · 2013
Earlier work this paper cites.
Unsupervised visual domain adaptation using subspace alignment
Basura Fernando, Amaury Habrard, Marc Sebban, and Tinne Tuytelaars. 2013 · 2013
Cited alongside, same era.
Wikidata: a free collaborative knowledgebase
Denny Vrandečić and Markus Krötzsch. 2014 · 2014
Cited alongside, same era.
Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy. 2015 · 2015
Cited alongside, same era.
Matching networks for one shot learning
Oriol Vinyals, Charles Blundell, Tim Lillicrap, Daan Wierstra, et al. 2016 · 2016
Cited alongside, same era.
Meta networks
Tsendsuren Munkhdalai and Hong Yu. 2017 · 2017
Cited alongside, same era.
Optimization as a model for few-shot learning
Sachin Ravi and Hugo Larochelle. 2017 · 2017
Cited alongside, same era.
Fewrel: A large-scale supervised few-shot relation classification dataset with state-of-the-art evaluation
Xu Han, Hao Zhu, Pengfei Yu, Ziyun Wang, Yuan Yao, Zhiyuan Liu, and Maosong Sun. 2018 · 2018
Later among the works it cites.
Few-shot learning with graph neural networks
Victor Garcia Satorras and Joan Bruna Estrach. 2018 · 2018
Later among the works it cites.
Adversarial multi-lingual neural relation extraction
Xiaozhi Wang, Xu Han, Yankai Lin, Zhiyuan Liu, and Maosong Sun. 2018 · 2018
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
Closest in time.
Hybrid attention-based prototypical networks for noisy few-shot relation classification
Tianyu Gao, Xu Han, Zhiyuan Liu, and Maosong Sun. 2019 · 2019
Closest in time.
Matching the blanks: Distributional similarity for relation learning
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard Zemel. 2017 · 2017
Cited alongside, same era.
Livio Baldini Soares, Nicholas FitzGerald, Jeffrey Ling, and Tom Kwiatkowski. 2019 · 2019
Closest in time.
Domain-adversarial training of neural networks
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky. 2016 · 2030
Closest in time.