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Active learning has been shown to be an effective way to alleviate some of the effort required in utilising large collections of unlabelled data for machine learning tasks without needing to fully label them.
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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Albert: A lite bert for self-supervised learning of language representations
Lan, Z., Chen, M., Goodman, S., Gimpel, K., Sharma, P., and Soricut, R. (2019) · 1909
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Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter
Sanh, V., Debut, L., Chaumond, J., and Wolf, T. (2019) · 1910
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A statistical interpretation of term specificity and its application in retrieval
Sparck Jones, K. (1972) · 1972
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Query by committee
Seung, H. S., Opper, M., and Sompolinsky, H. (1992) · 1992
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A sequential algorithm for training text classifiers
Lewis, D. D. and Gale, W. A. (1994) · 1994
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Active learning with committees for text categorization
Liere, R. and Tadepalli, P. (1997) · 1997
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Query learning strategies using boosting and bagging
Mamitsuka, N. A. H. et al. (1998) · 1998
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Support vector machine active learning for image retrieval
Tong, S. and Chang, E. (2001) · 2001
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Support vector machine active learning with applications to text classification
Tong, S. and Koller, D. (2001) · 2001
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An active learning framework for content-based information retrieval
Zhang, C. and Chen, T. (2002) · 2002
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Latent dirichlet allocation
Blei, D. M., Ng, A. Y., and Jordan, M. I. (2003) · 2003
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Hsu, C.-W., Chang, C.-C., Lin, C.-J., et al. (2003). A practical guide to support vector classification
2003
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A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts
Pang, B. and Lee, L. (2004) · 2004
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Combining active and semi-supervised learning for spoken language understanding
Tur, G., Hakkani-Tür, D., and Schapire, R. E. (2005) · 2005
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Large-scale text categorization by batch mode active learning
Hoi, S. C., Jin, R., and Lyu, M. R. (2006) · 2006
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Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification
Blitzer, J., Dredze, M., and Pereira, F. (2007) · 2007
Cited alongside, same era.
A holistic lexicon-based approach to opinion mining
Ding, X., Liu, B., and Yu, P. S. (2008) · 2008
Cited alongside, same era.
Sweetening the dataset: Using active learning to label unlabelled datasets
Hu, R., Mac Namee, B., and Delany, S. J. (2008) · 2008
Cited alongside, same era.
An analysis of active learning strategies for sequence labeling tasks
Settles, B. and Craven, M. (2008) · 2008
Cited alongside, same era.
Settles, B. (2009). Active learning literature survey. Tech. rep., University of Wisconsin-Madison Department of Computer Sciences
2009
Cited alongside, same era.
Egal: Exploration guided active learning for tcbr
Bag of tricks for efficient text classification
Joulin, A., Grave, E., Bojanowski, P., and Mikolov, T. (2016) · 2016
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Enriching word vectors with subword information
Bojanowski, P., Grave, E., Joulin, A., and Mikolov, T. (2017) · 2017
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Learned in translation: Contextualized word vectors
McCann, B., Bradbury, J., Xiong, C., and Socher, R. (2017) · 2017
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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
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Active discriminative text representation learning
Zhang, Y., Lease, M., and Wallace, B. C. (2017) · 2017
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Zhao, W. (2017). Deep active learning for short-text classification
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Hu, R., Delany, S. J., and Mac Namee, B. (2010) · 2010
Cited alongside, same era.
Improving gender classification of blog authors
Mukherjee, A. and Liu, B. (2010) · 2010
Cited alongside, same era.
Active learning for biomedical citation screening
Wallace, B. C., Small, K., Brodley, C. E., and Trikalinos, T. A. (2010) · 2010
Cited alongside, same era.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J. (2014) · 2014
Cited alongside, same era.
Distributed representations of sentences and documents
Le, Q. and Mikolov, T. (2014) · 2014
Cited alongside, same era.
Reducing systematic review workload through certainty-based screening
Miwa, M., Thomas, J., O’Mara-Eves, A., and Ananiadou, S. (2014) · 2014
Cited alongside, same era.
Glove: Global vectors for word representation
Pennington, J., Socher, R., and Manning, C. (2014) · 2014
Cited alongside, same era.
2017
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Stability of topic modeling via matrix factorization
Belford, M., Mac Namee, B., and Greene, D. (2018) · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2018) · 2018
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Universal language model fine-tuning for text classification
Howard, J. and Ruder, S. (2018) · 2018
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Deep contextualized word representations
Peters, M. E., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., and Zettlemoyer, L. (2018) · 2018
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Improving language understanding by generative pre-training
Radford, A., Narasimhan, K., Salimans, T., and Sutskever, I. (2018) · 2018
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Siddhant, A. and Lipton, Z. C. (2018) · 2018
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Improving active learning in systematic reviews
Singh, G., Thomas, J., and Shawe-Taylor, J. (2018) · 2018
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Language models are unsupervised multitask learners
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., and Sutskever, I. (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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Zhang, Y. (2019). Neural nlp models under low-supervision scenarios
2019
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