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In this work, we provide a survey of active learning (AL) for its applications in natural language processing (NLP).
Diverse mini-batch active learning
Fedor Zhdanov. 2019 · 1901
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Discriminative active learning
Daniel Gissin and Shai Shalev-Shwartz. 2019 · 1907
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A mathematical theory of communication
Claude Elwood Shannon. 1948 · 1948
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Elementary applied statistics: for students in behavioral science
Linton C Freeman. 1965 · 1965
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Query by committee
H Sebastian Seung, Manfred Opper, and Haim Sompolinsky. 1992 · 1992
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Improving generalization with active learning
David Cohn, Les Atlas, and Richard Ladner. 1994 · 1994
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Heterogeneous uncertainty sampling for supervised learning
David D Lewis and Jason Catlett. 1994 · 1994
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A sequential algorithm for training text classifiers
David D Lewis and William A Gale. 1994 · 1994
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Active 2 learning: Actively reducing redundancies in active learning methods for sequence tagging and machine translation
Rishi Hazra, Parag Dutta, Shubham Gupta, Mohammed Abdul Qaathir, and Ambedkar Dukkipati. 2021 · 1995
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Active learning with statistical models
David A Cohn, Zoubin Ghahramani, and Michael I Jordan. 1996 · 1996
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Minimizing manual annotation cost in supervised training from corpora
Sean P. Engelson and Ido Dagan. 1996 · 1996
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Selective sampling for example-based word sense disambiguation
Atsushi Fujii, Kentaro Inui, Takenobu Tokunaga, and Hozumi Tanaka. 1998 · 1998
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Employing em and pool-based active learning for text classification
Andrew McCallum and Kamal Nigam. 1998 · 1998
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Active learning for natural language parsing and information extraction
Cynthia A Thompson, Mary Elaine Califf, and Raymond J Mooney. 1999 · 1999
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Sample selection for statistical grammar induction
Rebecca Hwa. 2000 · 2000
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Rule writing or annotation: Cost-efficient resource usage for base noun phrase chunking
Grace Ngai and David Yarowsky. 2000 · 2000
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Less is more: Active learning with support vector machines
Greg Schohn and David Cohn. 2000 · 2000
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Toward optimal active learning through sampling estimation of error reduction
Nicholas Roy and Andrew McCallum. 2001 · 2001
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Active hidden markov models for information extraction
Tobias Scheffer, Christian Decomain, and Stefan Wrobel. 2001 · 2001
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Support vector machine active learning with applications to text classification
Simon Tong and Daphne Koller. 2001 · 2001
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An empirical study of active learning with support vector machines forJapanese word segmentation
Manabu Sassano. 2002 · 2002
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Active learning for statistical natural language parsing
Min Tang, Xiaoqiang Luo, and Salim Roukos. 2002 · 2002
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Active learning for HPSG parse selection
Jason Baldridge and Miles Osborne. 2003 · 2003
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Incorporating diversity in active learning with support vector machines
Klaus Brinker. 2003 · 2003
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Representative sampling for text classification using support vector machines
Zhao Xu, Kai Yu, Volker Tresp, Xiaowei Xu, and Jizhi Wang. 2003 · 2003
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Active learning and the total cost of annotation
Jason Baldridge and Miles Osborne. 2004 · 2004
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A survey of outlier detection methodologies
Victoria Hodge and Jim Austin. 2004 · 2004
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Sample selection for statistical parsing
Rebecca Hwa. 2004 · 2004
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Using cluster-based sampling to select initial training set for active learning in text classification
Jaeho Kang, Kwang Ryel Ryu, and Hyuk-Chul Kwon. 2004 · 2004
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Active learning using pre-clustering
Hieu T Nguyen and Arnold Smeulders. 2004 · 2004
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Multi-criteria-based active learning for named entity recognition
Dan Shen, Jie Zhang, Jian Su, Guodong Zhou, and Chew-Lim Tan. 2004 · 2004
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Reducing labeling effort for structured prediction tasks
Aron Culotta and Andrew McCallum. 2005 · 2005
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Low cost portability for statistical machine translation based on n-gram frequency and TF-IDF
Matthias Eck, Stephan Vogel, and Alex Waibel. 2005 · 2005
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Investigating the effects of selective sampling on the annotation task
Ben Hachey, Beatrice Alex, and Markus Becker. 2005 · 2005
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An empirical study of the behavior of active learning for word sense disambiguation
Jinying Chen, Andrew Schein, Lyle Ungar, and Martha Palmer. 2006 · 2006
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MMR-based active machine learning for bio named entity recognition
Seokhwan Kim, Yu Song, Kyungduk Kim, Jeong-Won Cha, and Gary Geunbae Lee. 2006 · 2006
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Margin-based active learning for structured output spaces
Dan Roth and Kevin Small. 2006 · 2006
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Active annotation
Andreas Vlachos. 2006 · 2006
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Domain adaptation with active learning for word sense disambiguation
Yee Seng Chan and Hwee Tou Ng. 2007 · 2007
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Dual strategy active learning
Pinar Donmez, Jaime G Carbonell, and Paul N Bennett. 2007 · 2007
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Learning on the border: active learning in imbalanced data classification
Seyda Ertekin, Jian Huang, Leon Bottou, and Lee Giles. 2007 · 2007
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Efficient computation of entropy gradient for semi-supervised conditional random fields
Gideon Mann and Andrew McCallum. 2007 · 2007
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Active learning for part-of-speech tagging: Accelerating corpus annotation
Eric Ringger, Peter McClanahan, Robbie Haertel, George Busby, Marc Carmen, James Carroll, Kevin Seppi, and Deryle Lonsdale. 2007 · 2007
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Active learning for logistic regression: an evaluation
Andrew I Schein and Lyle H Ungar. 2007 · 2007
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Multiple-instance active learning
Burr Settles, Mark Craven, and Soumya Ray. 2007 · 2007
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An approach to text corpus construction which cuts annotation costs and maintains reusability of annotated data
Katrin Tomanek, Joachim Wermter, and Udo Hahn. 2007 · 2007
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Active learning for word sense disambiguation with methods for addressing the class imbalance problem
Jingbo Zhu and Eduard Hovy. 2007 · 2007
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Proactive learning: cost-sensitive active learning with multiple imperfect oracles
Pinar Donmez and Jaime G Carbonell. 2008 · 2008
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Stopping criteria for active learning of named entity recognition
Florian Laws and Hinrich Schütze. 2008 · 2008
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Multi-task active learning for linguistic annotations
Roi Reichart, Katrin Tomanek, Udo Hahn, and Ari Rappoport. 2008 · 2008
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Assessing the costs of machine-assisted corpus annotation through a user study
Eric Ringger, Marc Carmen, Robbie Haertel, Kevin Seppi, Deryle Lonsdale, Peter McClanahan, James Carroll, and Noel Ellison. 2008 · 2008
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Active learning for pipeline models
Dan Roth and Kevin Small. 2008 · 2008
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A survey of active learning for text classification using deep neural networks
Christopher Schröder and Andreas Niekler. 2020 · 2008
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An analysis of active learning strategies for sequence labeling tasks
Burr Settles and Mark Craven. 2008 · 2008
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Active learning with real annotation costs
Burr Settles, Mark Craven, and Lewis Friedland. 2008 · 2008
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Actively transfer domain knowledge
Xiaoxiao Shi, Wei Fan, and Jiangtao Ren. 2008 · 2008
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Cheap and fast – but is it good? evaluating non-expert annotations for natural language tasks
Rion Snow, Brendan O’Connor, Daniel Jurafsky, and Andrew Ng. 2008 · 2008
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Approximating learning curves for active-learning-driven annotation
Katrin Tomanek and Udo Hahn. 2008 · 2008
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A stopping criterion for active learning
Andreas Vlachos. 2008 · 2008
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Multi-criteria-based strategy to stop active learning for data annotation
Jingbo Zhu, Huizhen Wang, and Eduard Hovy. 2008b · 2008
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Active learning with sampling by uncertainty and density for word sense disambiguation and text classification
Jingbo Zhu, Huizhen Wang, Tianshun Yao, and Benjamin K Tsou. 2008c · 2008
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Estimating annotation cost for active learning in a multi-annotator environment
Shilpa Arora, Eric Nyberg, and Carolyn P. Rosé. 2009 · 2009
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How well does active learning actually
Jason Baldridge and Alexis Palmer. 2009 · 2009
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Curriculum learning
Yoshua Bengio, Jérôme Louradour, Ronan Collobert, and Jason Weston. 2009 · 2009
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A method for stopping active learning based on stabilizing predictions and the need for user-adjustable stopping
Michael Bloodgood and K. Vijay-Shanker. 2009a · 2009
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Taking into account the differences between actively and passively acquired data: The case of active learning with support vector machines for imbalanced datasets
Michael Bloodgood and K. Vijay-Shanker. 2009b · 2009
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Mine the easy, classify the hard: A semi-supervised approach to automatic sentiment classification
Sajib Dasgupta and Vincent Ng. 2009 · 2009
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Active learning by labeling features
Gregory Druck, Burr Settles, and Andrew McCallum. 2009 · 2009
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Active learning for anaphora resolution
Caroline Gasperin. 2009 · 2009
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Active learning for statistical phrase-based machine translation
Gholamreza Haffari, Maxim Roy, and Anoop Sarkar. 2009 · 2009
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Active learning for multilingual statistical machine translation
Gholamreza Haffari and Anoop Sarkar. 2009 · 2009
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A combination of active learning and semi-supervised learning starting with positive and unlabeled examples for word sense disambiguation: An empirical study on Japanese web search query
Makoto Imamura, Yasuhiro Takayama, Nobuhiro Kaji, Masashi Toyoda, and Masaru Kitsuregawa. 2009 · 2009
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Active dual supervision: Reducing the cost of annotating examples and features
Prem Melville and Vikas Sindhwani. 2009 · 2009
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A literature survey of active machine learning in the context of natural language processing
Fredrik Olsson. 2009 · 2009
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An intrinsic stopping criterion for committee-based active learning
Fredrik Olsson and Katrin Tomanek. 2009 · 2009
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Sample selection for statistical parsers: Cognitively driven algorithms and evaluation measures
Roi Reichart and Ari Rappoport. 2009 · 2009
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Active learning literature survey
Burr Settles. 2009 · 2009
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On proper unit selection in active learning: Co-selection effects for named entity recognition
Katrin Tomanek, Florian Laws, Udo Hahn, and Hinrich Schütze. 2009 · 2009
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A web survey on the use of active learning to support annotation of text data
Katrin Tomanek and Fredrik Olsson. 2009 · 2009
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Active learning with sampling by uncertainty and density for data annotations
Jingbo Zhu, Huizhen Wang, Benjamin K Tsou, and Matthew Ma. 2009 · 2009
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Active learning-based elicitation for semi-supervised word alignment
Vamshi Ambati, Stephan Vogel, and Jaime Carbonell. 2010b · 2010
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Active semi-supervised learning for improving word alignment
Vamshi Ambati, Stephan Vogel, and Jaime Carbonell. 2010c · 2010
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Discriminative sample selection for statistical machine translation
Sankaranarayanan Ananthakrishnan, Rohit Prasad, David Stallard, and Prem Natarajan. 2010a · 2010
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Active learning for building a corpus of questions for parsing
Jordi Atserias, Giuseppe Attardi, Maria Simi, and Hugo Zaragoza. 2010 · 2010
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Bucking the trend: Large-scale cost-focused active learning for statistical machine translation
Michael Bloodgood and Chris Callison-Burch. 2010 · 2010
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D-confidence: An active learning strategy which efficiently identifies small classes
Nuno Escudeiro and Alípio Jorge. 2010 · 2010
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Using variance as a stopping criterion for active learning of frame assignment
Masood Ghayoomi. 2010 · 2010
Cited alongside, same era.
Parallel active learning: Eliminating wait time with minimal staleness
Robbie Haertel, Paul Felt, Eric K. Ringger, and Kevin Seppi. 2010 · 2010
Cited alongside, same era.
Off to a good start: Using clustering to select the initial training set in active learning
Rong Hu, Brian Mac Namee, and Sarah Jane Delany. 2010 · 2010
Cited alongside, same era.
Phrase-based statistical language generation using graphical models and active learning
François Mairesse, Milica Gašić, Filip Jurčíček, Simon Keizer, Blaise Thomson, Kai Yu, and Steve Young. 2010 · 2010
Cited alongside, same era.
Domain adaptation meets active learning
Piyush Rai, Avishek Saha, Hal Daumé, and Suresh Venkatasubramanian. 2010 · 2010
Cited alongside, same era.
Bringing active learning to life
Competence-based curriculum learning for neural machine translation
Emmanouil Antonios Platanios, Otilia Stretcu, Graham Neubig, Barnabas Poczos, and Tom Mitchell. 2019 · 2019
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Sampling bias in deep active classification: An empirical study
Ameya Prabhu, Charles Dognin, and Maneesh Singh. 2019 · 2019
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Active learning via membership query synthesis for semi-supervised sentence classification
Raphael Schumann and Ines Rehbein. 2019 · 2019
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Variational adversarial active learning
Samarth Sinha, Sayna Ebrahimi, and Trevor Darrell. 2019 · 2019
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Learning how to active learn by dreaming
Thuy-Trang Vu, Ming Liu, Dinh Phung, and Gholamreza Haffari. 2019 · 2019
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Learning loss for active learning
Donggeun Yoo and In So Kweon. 2019 · 2019
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Ines Rehbein, Josef Ruppenhofer, and Alexis Palmer. 2010 · 2010
Cited alongside, same era.
Using smaller constituents rather than sentences in active learning for Japanese dependency parsing
Manabu Sassano and Sadao Kurohashi. 2010 · 2010
Cited alongside, same era.
From theories to queries: Active learning in practice
Burr Settles. 2011 · 2010
Cited alongside, same era.
A comparison of models for cost-sensitive active learning
Katrin Tomanek and Udo Hahn. 2010 · 2010
Cited alongside, same era.
Inactive learning? difficulties employing active learning in practice
Josh Attenberg and Foster Provost. 2011 · 2011
Cited alongside, same era.
Enhancing active learning for semantic role labeling via compressed dependency trees
Chenhua Chen, Alexis Palmer, and Caroline Sporleder. 2011 · 2011
Cited alongside, same era.
Two faces of active learning
Sanjoy Dasgupta. 2011 · 2011
Cited alongside, same era.
Later among the works it cites.
Empirical evaluation of active learning techniques for neural MT
Xiangkai Zeng, Sarthak Garg, Rajen Chatterjee, Udhyakumar Nallasamy, and Matthias Paulik. 2019 · 2019
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On warm-starting neural network training
Jordan Ash and Ryan P Adams. 2020 · 2020
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Deep batch active learning by diverse, uncertain gradient lower bounds
Jordan T. Ash, Chicheng Zhang, Akshay Krishnamurthy, John Langford, and Alekh Agarwal. 2020 · 2020
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Optimizing annotation effort using active learning strategies: A sentiment analysis case study in Persian
Seyed Arad Ashrafi Asli, Behnam Sabeti, Zahra Majdabadi, Preni Golazizian, Reza Fahmi, and Omid Momenzadeh. 2020 · 2020
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Pre-trained language model based active learning for sentence matching
Guirong Bai, Shizhu He, Kang Liu, Jun Zhao, and Zaiqing Nie. 2020 · 2020
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Cost-quality adaptive active learning for chinese clinical named entity recognition
Tingting Cai, Yangming Zhou, and Hong Zheng. 2020 · 2020
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Using error decay prediction to overcome practical issues of deep active learning for named entity recognition
Haw-Shiuan Chang, Shankar Vembu, Sunil Mohan, Rheeya Uppaal, and Andrew McCallum. 2020 · 2020
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Active Learning for BERT: An Empirical Study
Liat Ein-Dor, Alon Halfon, Ariel Gera, Eyal Shnarch, Lena Dankin, Leshem Choshen, Marina Danilevsky, Ranit Aharonov, Yoav Katz, and Noam Slonim. 2020 · 2020
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Enhanced labelling in active learning for coreference resolution
Vebjørn Espeland, Beatrice Alex, and Benjamin Bach. 2020 · 2020
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Fine-tuning BERT for low-resource natural language understanding via active learning
Daniel Grießhaber, Johannes Maucher, and Ngoc Thang Vu. 2020 · 2020
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Don’t stop pretraining: Adapt language models to domains and tasks
Suchin Gururangan, Ana Marasović, Swabha Swayamdipta, Kyle Lo, Iz Beltagy, Doug Downey, and Noah A. Smith. 2020 · 2020
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Camouflaged Chinese spam content detection with semi-supervised generative active learning
Zhuoren Jiang, Zhe Gao, Yu Duan, Yangyang Kang, Changlong Sun, Qiong Zhang, and Xiaozhong Liu. 2020 · 2020
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Deep active learning for sequence labeling based on diversity and uncertainty in gradient
Yekyung Kim. 2020 · 2020
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Active learning for coreference resolution using discrete annotation
Belinda Z. Li, Gabriel Stanovsky, and Luke Zettlemoyer. 2020 · 2020
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ALICE: Active learning with contrastive natural language explanations
Weixin Liang, James Zou, and Zhou Yu. 2020 · 2020
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On the Importance of Adaptive Data Collection for Extremely Imbalanced Pairwise Tasks
Stephen Mussmann, Robin Jia, and Percy Liang. 2020 · 2020
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Merging weak and active supervision for semantic parsing
Ansong Ni, Pengcheng Yin, and Graham Neubig. 2020 · 2020
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Learning structured representations of entity names using Active Learning and weak supervision
Kun Qian, Poornima Chozhiyath Raman, Yunyao Li, and Lucian Popa. 2020 · 2020
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Textual data augmentation for efficient active learning on tiny datasets
Husam Quteineh, Spyridon Samothrakis, and Richard Sutcliffe. 2020 · 2020
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Active sentence learning by adversarial uncertainty sampling in discrete space
Dongyu Ru, Jiangtao Feng, Lin Qiu, Hao Zhou, Mingxuan Wang, Weinan Zhang, Yong Yu, and Lei Li. 2020 · 2020
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Uncertainty and traffic-aware active learning for semantic parsing
Priyanka Sen and Emine Yilmaz. 2020 · 2020
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Dataset cartography: Mapping and diagnosing datasets with training dynamics
Swabha Swayamdipta, Roy Schwartz, Nicholas Lourie, Yizhong Wang, Hannaneh Hajishirzi, Noah A. Smith, and Yejin Choi. 2020 · 2020
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Predicting performance for natural language processing tasks
Mengzhou Xia, Antonios Anastasopoulos, Ruochen Xu, Yiming Yang, and Graham Neubig. 2020 · 2020
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Cold-start active learning through self-supervised language modeling
Michelle Yuan, Hsuan-Tien Lin, and Jordan Boyd-Graber. 2020 · 2020
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SeqMix: Augmenting active sequence labeling via sequence mixup
Rongzhi Zhang, Yue Yu, and Chao Zhang. 2020 · 2020
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Active learning approaches to enhancing neural machine translation
Yuekai Zhao, Haoran Zhang, Shuchang Zhou, and Zhihua Zhang. 2020b · 2020
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A multitask active learning framework for natural language understanding
Hua Zhu, Wu Ye, Sihan Luo, and Xidong Zhang. 2020 · 2020
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Ne–lp: normalized entropy-and loss prediction-based sampling for active learning in chinese word segmentation on ehrs
Tingting Cai, Zhiyuan Ma, Hong Zheng, and Yangming Zhou. 2021 · 2021
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Active learning for assisted corpus construction: A case study in knowledge discovery from biomedical text
Hian Cañizares-Díaz, Alejandro Piad-Morffis, Suilan Estevez-Velarde, Yoan Gutiérrez, Yudivián Almeida Cruz, Andres Montoyo, and Rafael Muñoz-Guillena. 2021 · 2021
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Reducing confusion in active learning for part-of-speech tagging
Aditi Chaudhary, Antonios Anastasopoulos, Zaid Sheikh, and Graham Neubig. 2021 · 2021
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Batch active learning at scale
Gui Citovsky, Giulia DeSalvo, Claudio Gentile, Lazaros Karydas, Anand Rajagopalan, Afshin Rostamizadeh, and Sanjiv Kumar. 2021 · 2021
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A survey of uncertainty in deep neural networks
Jakob Gawlikowski, Cedrique Rovile Njieutcheu Tassi, Mohsin Ali, Jongseok Lee, Matthias Humt, Jianxiang Feng, Anna Kruspe, Rudolph Triebel, Peter Jung, Ribana Roscher, et al. 2021 · 2021
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Investigating active learning in interactive neural machine translation
Kamal Gupta, Dhanvanth Boppana, Rejwanul Haque, Asif Ekbal, and Pushpak Bhattacharyya. 2021 · 2021
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Multi-domain active learning: A comparative study
Rui He, Shan He, and Ke Tang. 2021 · 2021
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Phrase-level active learning for neural machine translation
Junjie Hu and Graham Neubig. 2021 · 2021
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Active curriculum learning
Borna Jafarpour, Dawn Sepehr, and Nick Pogrebnyakov. 2021 · 2021
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Mind your outliers! investigating the negative impact of outliers on active learning for visual question answering
Siddharth Karamcheti, Ranjay Krishna, Li Fei-Fei, and Christopher Manning. 2021 · 2021
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Active learning and negative evidence for language identification
Thomas Lippincott and Ben Van Durme. 2021 · 2021
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ActiveEA: Active learning for neural entity alignment
Bing Liu, Harrisen Scells, Guido Zuccon, Wen Hua, and Genghong Zhao. 2021 · 2021
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Active learning for interactive relation extraction in a French newspaper’s articles
Cyrielle Mallart, Michel Le Nouy, Guillaume Gravier, and Pascale Sébillot. 2021 · 2021
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Active learning by acquiring contrastive examples
Katerina Margatina, Giorgos Vernikos, Loïc Barrault, and Nikolaos Aletras. 2021 · 2021
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Efficient deep learning: A survey on making deep learning models smaller, faster, and better
Gaurav Menghani. 2021 · 2021
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Tuning deep active learning for semantic role labeling
Skatje Myers and Martha Palmer. 2021 · 2021
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Hitting the target: Stopping active learning at the cost-based optimum
Zac Pullar-Strecker, Katharina Dost, Eibe Frank, and Jörg Wicker. 2021 · 2021
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Subsequence based deep active learning for named entity recognition
Puria Radmard, Yassir Fathullah, and Aldo Lipani. 2021 · 2021
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A survey of deep active learning
Pengzhen Ren, Yun Xiao, Xiaojun Chang, Po-Yao Huang, Zhihui Li, Brij B Gupta, Xiaojiang Chen, and Xin Wang. 2021 · 2021
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Active learning for sequence tagging with deep pre-trained models and Bayesian uncertainty estimates
Artem Shelmanov, Dmitri Puzyrev, Lyubov Kupriyanova, Denis Belyakov, Daniil Larionov, Nikita Khromov, Olga Kozlova, Ekaterina Artemova, Dmitry V. Dylov, and Alexander Panchenko. 2021 · 2021
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Active learning for event extraction with memory-based loss prediction model
Shirong Shen, Zhen Li, and Guilin Qi. 2021 · 2021
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Diversity-aware batch active learning for dependency parsing
Tianze Shi, Adrian Benton, Igor Malioutov, and Ozan İrsoy. 2021 · 2021
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Diversity enhanced active learning with strictly proper scoring rules
Wei Tan, Lan Du, and Wray Buntine. 2021 · 2021
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Putting humans in the natural language processing loop: A survey
Zijie J. Wang, Dongjin Choi, Shenyu Xu, and Diyi Yang. 2021 · 2021
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Cartography active learning
Mike Zhang and Barbara Plank. 2021 · 2021
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Uncertainty-aware active learning for optimal bayesian classifier
Guang Zhao, Edward Dougherty, Byung-Jun Yoon, Francis Alexander, and Xiaoning Qian. 2021 · 2021
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Active learning for massively parallel translation of constrained text into low resource languages
Zhong Zhou and Alex Waibel. 2021 · 2021
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COMET-QE and active learning for low-resource machine translation
Everlyn Chimoto and Bruce Bassett. 2022 · 2022
Closest in time.
AfroLM: A self-active learning-based multilingual pretrained language model for 23 African languages
Bonaventure F. P. Dossou, Atnafu Tonja, Oreen Yousuf, Salomey Osei, Abigail Oppong, Iyanuoluwa Shode, Oluwabusayo Olufunke Awoyomi, and Chris Emezue. 2022 · 2022
Closest in time.
Efficient argument structure extraction with transfer learning and active learning
Xinyu Hua and Lu Wang. 2022 · 2022
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Impact of stop sets on stopping active learning for text classification
Luke Kurlandski and Michael Bloodgood. 2022 · 2022
Closest in time.
CrudeOilNews: An annotated crude oil news corpus for event extraction
Meisin Lee, Lay-Ki Soon, Eu Gene Siew, and Ly Fie Sugianto. 2022 · 2022
Closest in time.
Ltp: A new active learning strategy for crf-based named entity recognition
Mingyi Liu, Zhiying Tu, Tong Zhang, Tonghua Su, Xiaofei Xu, and Zhongjie Wang. 2022 · 2022
Closest in time.
Active learning over multiple domains in natural language tasks
Shayne Longpre, Julia Reisler, Edward Greg Huang, Yi Lu, Andrew Frank, Nikhil Ramesh, and Chris DuBois. 2022 · 2022
Closest in time.
Low-resource interactive active labeling for fine-tuning language models
Seiji Maekawa, Dan Zhang, Hannah Kim, Sajjadur Rahman, and Estevam Hruschka. 2022 · 2022
Closest in time.
On the importance of effectively adapting pretrained language models for active learning
Katerina Margatina, Loic Barrault, and Nikolaos Aletras. 2022 · 2022
Closest in time.
Onception: Active learning with expert advice for real world machine translation
Vânia Mendonça, Ricardo Rei, Luisa Coheur, and Alberto Sardinha. 2022 · 2022
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Active evaluation: Efficient NLG evaluation with few pairwise comparisons
Akash Kumar Mohankumar and Mitesh Khapra. 2022 · 2022
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On efficiently acquiring annotations for multilingual models
Joel Moniz, Barun Patra, and Matthew Gormley. 2022 · 2022
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FAMIE: A fast active learning framework for multilingual information extraction
Minh Van Nguyen, Nghia Ngo, Bonan Min, and Thien Nguyen. 2022 · 2022
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Multi-task active learning for pre-trained transformer-based models
Guy Rotman and Roi Reichart. 2022 · 2022
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Revisiting uncertainty-based query strategies for active learning with transformers
Christopher Schröder, Andreas Niekler, and Martin Potthast. 2022 · 2022
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Active learning on pre-trained language model with task-independent triplet loss
Seungmin Seo, Donghyun Kim, Youbin Ahn, and Kyong-Ho Lee. 2022 · 2022
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Active learning helps pretrained models learn the intended task
Alex Tamkin, Dat Nguyen, Salil Deshpande, Jesse Mu, and Noah Goodman. 2022 · 2022
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Active learning for abstractive text summarization
Akim Tsvigun, Ivan Lysenko, Danila Sedashov, Ivan Lazichny, Eldar Damirov, Vladimir Karlov, Artemy Belousov, Leonid Sanochkin, Maxim Panov, Alexander Panchenko, Mikhail Burtsev, and Artem Shelmanov. 2022a · 2022
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Towards computationally feasible deep active learning
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