Rethinking Semi-supervised Learning with Language Models
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Francesco Tonolini | G. Kazai | Yunlong Jiao | Nikolaos Aletras | Emine Yilmaz | Zhengxiang Shi | Zhengxiang Shi | Francesco Tonolini
[1] Aldo Lipani,et al. Don't Stop Pretraining? Make Prompt-based Fine-tuning Powerful Learner , 2023, ArXiv.
[2] L. Ein-Dor,et al. Zero-Shot Text Classification with Self-Training , 2022, EMNLP.
[3] B. Schiele,et al. USB: A Unified Semi-supervised Learning Benchmark , 2022, NeurIPS.
[4] Zejiang Hou,et al. Meta-Learning the Difference: Preparing Large Language Models for Efficient Adaptation , 2022, Transactions of the Association for Computational Linguistics.
[5] Yue Feng,et al. Learning to Execute Actions or Ask Clarification Questions , 2022, NAACL-HLT.
[6] Qiang Zhang,et al. StepGame: A New Benchmark for Robust Multi-Hop Spatial Reasoning in Texts , 2022, AAAI.
[7] Lisa Anne Hendricks,et al. Training Compute-Optimal Large Language Models , 2022, ArXiv.
[8] Jianmin Wang,et al. Debiased Self-Training for Semi-Supervised Learning , 2022, NeurIPS.
[9] Jordan Massiah,et al. PARS: Pseudo-Label Aware Robust Sample Selection for Learning with Noisy Labels , 2022, ArXiv.
[10] M. de Rijke,et al. Extending CLIP for Category-to-image Retrieval in E-commerce , 2021, ECIR.
[11] Nicholas Carlini,et al. AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation , 2021, ICLR.
[12] Loïc Barrault,et al. On the Importance of Effectively Adapting Pretrained Language Models for Active Learning , 2021, ACL.
[13] T. Shinozaki,et al. FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labeling , 2021, NeurIPS.
[14] Xifeng Yan,et al. Task-adaptive Pre-training and Self-training are Complementary for Natural Language Understanding , 2021, EMNLP.
[15] Rong Jin,et al. Dash: Semi-Supervised Learning with Dynamic Thresholding , 2021, ICML.
[16] Mingsheng Long,et al. Self-Tuning for Data-Efficient Deep Learning , 2021, ICML.
[17] Danqi Chen,et al. Making Pre-trained Language Models Better Few-shot Learners , 2021, ACL.
[18] Gabriel Synnaeve,et al. Self-Training and Pre-Training are Complementary for Speech Recognition , 2020, ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[19] Colin Raffel,et al. mT5: A Massively Multilingual Pre-trained Text-to-Text Transformer , 2020, NAACL.
[20] Timo Schick,et al. Exploiting Cloze-Questions for Few-Shot Text Classification and Natural Language Inference , 2020, EACL.
[21] Jihong Ouyang,et al. Semi-Supervised Text Classification with Balanced Deep Representation Distributions , 2021, ACL.
[22] Chao Zhang,et al. Weakly-Supervised Text Classification Using Label Names Only , 2020, EMNLP.
[23] Diyi Yang,et al. Local Additivity Based Data Augmentation for Semi-supervised NER , 2020, EMNLP.
[24] Dilek Z. Hakkani-Tür,et al. DialoGLUE: A Natural Language Understanding Benchmark for Task-Oriented Dialogue , 2020, ArXiv.
[25] Subhabrata Mukherjee,et al. Uncertainty-aware Self-training for Few-shot Text Classification , 2020, NeurIPS.
[26] Quoc V. Le,et al. Rethinking Pre-training and Self-training , 2020, NeurIPS.
[27] Barbara Plank,et al. Neural Unsupervised Domain Adaptation in NLP—A Survey , 2020, COLING.
[28] Diyi Yang,et al. MixText: Linguistically-Informed Interpolation of Hidden Space for Semi-Supervised Text Classification , 2020, ACL.
[29] Doug Downey,et al. Don’t Stop Pretraining: Adapt Language Models to Domains and Tasks , 2020, ACL.
[30] Alec Radford,et al. Scaling Laws for Neural Language Models , 2020, ArXiv.
[31] David Berthelot,et al. FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence , 2020, NeurIPS.
[32] Quoc V. Le,et al. Self-Training With Noisy Student Improves ImageNet Classification , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[33] Marc'Aurelio Ranzato,et al. Revisiting Self-Training for Neural Sequence Generation , 2019, ICLR.
[34] Quoc V. Le,et al. Unsupervised Data Augmentation for Consistency Training , 2019, NeurIPS.
[35] Nicholas Carlini,et al. ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring , 2019, ArXiv.
[36] Kathleen McKeown,et al. A Good Sample is Hard to Find: Noise Injection Sampling and Self-Training for Neural Language Generation Models , 2019, INLG.
[37] Mirella Lapata,et al. Semi-Supervised Semantic Role Labeling with Cross-View Training , 2019, EMNLP.
[38] Gerard de Melo,et al. A Robust Self-Learning Framework for Cross-Lingual Text Classification , 2019, EMNLP.
[39] Omer Levy,et al. RoBERTa: A Robustly Optimized BERT Pretraining Approach , 2019, ArXiv.
[40] Luo Si,et al. Semi-supervised Domain Adaptation for Dependency Parsing , 2019, ACL.
[41] Ming-Wei Chang,et al. Zero-Shot Entity Linking by Reading Entity Descriptions , 2019, ACL.
[42] Noah A. Smith,et al. Variational Pretraining for Semi-supervised Text Classification , 2019, ACL.
[43] David Berthelot,et al. MixMatch: A Holistic Approach to Semi-Supervised Learning , 2019, NeurIPS.
[44] Wei-Hung Weng,et al. Publicly Available Clinical BERT Embeddings , 2019, Proceedings of the 2nd Clinical Natural Language Processing Workshop.
[45] Myle Ott,et al. fairseq: A Fast, Extensible Toolkit for Sequence Modeling , 2019, NAACL.
[46] Iz Beltagy,et al. SciBERT: A Pretrained Language Model for Scientific Text , 2019, EMNLP.
[47] Shin Ishii,et al. Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[48] Ilya Sutskever,et al. Language Models are Unsupervised Multitask Learners , 2019 .
[49] Ming-Wei Chang,et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.
[50] Quoc V. Le,et al. Semi-Supervised Sequence Modeling with Cross-View Training , 2018, EMNLP.
[51] Eneko Agirre,et al. A robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings , 2018, ACL.
[52] Omer Levy,et al. GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding , 2018, BlackboxNLP@EMNLP.
[53] Barbara Plank,et al. Strong Baselines for Neural Semi-Supervised Learning under Domain Shift , 2018, ACL.
[54] Sebastian Ruder,et al. Universal Language Model Fine-tuning for Text Classification , 2018, ACL.
[55] Tatsuya Harada,et al. Maximum Classifier Discrepancy for Unsupervised Domain Adaptation , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[56] Harri Valpola,et al. Weight-averaged consistency targets improve semi-supervised deep learning results , 2017, ArXiv.
[57] Timo Aila,et al. Temporal Ensembling for Semi-Supervised Learning , 2016, ICLR.
[58] Xiang Zhang,et al. Character-level Convolutional Networks for Text Classification , 2015, NIPS.
[59] Jure Leskovec,et al. Hidden factors and hidden topics: understanding rating dimensions with review text , 2013, RecSys.
[60] Dong-Hyun Lee,et al. Pseudo-Label : The Simple and Efficient Semi-Supervised Learning Method for Deep Neural Networks , 2013 .
[61] Guodong Zhou,et al. Semi-Supervised Learning for Imbalanced Sentiment Classification , 2011, IJCAI.
[62] Christopher Potts,et al. Learning Word Vectors for Sentiment Analysis , 2011, ACL.
[63] Koby Crammer,et al. A theory of learning from different domains , 2010, Machine Learning.
[64] Jason Weston,et al. Curriculum learning , 2009, ICML '09.
[65] Ming-Wei Chang,et al. Importance of Semantic Representation: Dataless Classification , 2008, AAAI.
[66] Rajat Raina,et al. Self-taught learning: transfer learning from unlabeled data , 2007, ICML '07.
[67] Eugene Charniak,et al. Effective Self-Training for Parsing , 2006, NAACL.
[68] Yoshua Bengio,et al. Semi-supervised Learning by Entropy Minimization , 2004, CAP.
[69] David Yarowsky,et al. Unsupervised Word Sense Disambiguation Rivaling Supervised Methods , 1995, ACL.