{"list":"other","total":82,"rows":[{"id":"1810.04805","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","created":"2018-10-11","cited_by_count":40399},{"id":"2005.14165","title":"Language Models are Few-Shot Learners","created":"2020-05-28","cited_by_count":32419},{"id":"1910.10683","title":"Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer","created":"2019-10-23","cited_by_count":14599},{"id":"2106.09685","title":"LoRA: Low-Rank Adaptation of Large Language Models","created":"2021-06-17","cited_by_count":14380},{"id":"2107.03374","title":"Evaluating Large Language Models Trained on Code","created":"2021-07-07","cited_by_count":8320},{"id":"1910.13461","title":"BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension","created":"2019-10-29","cited_by_count":5893},{"id":"1703.03400","title":"Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks","created":"2017-03-09","cited_by_count":4897},{"id":"1703.05175","title":"Prototypical Networks for Few-shot Learning","created":"2017-03-15","cited_by_count":3015},{"id":"2107.13586","title":"Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing","created":"2021-07-28","cited_by_count":2234},{"id":"2103.10360","title":"GLM: General Language Model Pretraining with Autoregressive Blank Infilling","created":"2021-03-18","cited_by_count":1031},{"id":"2004.05439","title":"Meta-Learning in Neural Networks: A Survey","created":"2020-04-11","cited_by_count":950},{"id":"2002.00388","title":"A Survey on Knowledge Graphs: Representation, Acquisition and Applications","created":"2020-02-02","cited_by_count":643},{"id":"2110.04596","title":"Deep Long-Tailed Learning: A Survey","created":"2021-10-09","cited_by_count":417},{"id":"2106.07139","title":"Pre-Trained Models: Past, Present and Future","created":"2021-06-14","cited_by_count":319},{"id":"1812.09449","title":"A Survey on Deep Learning for Named Entity Recognition","created":"2018-12-22","cited_by_count":282},{"id":"1906.03158","title":"Matching the Blanks: Distributional Similarity for Relation Learning","created":"2019-06-07","cited_by_count":272},{"id":"1909.03546","title":"Entity, Relation, and Event Extraction with Contextualized Span Representations","created":"2019-09-08","cited_by_count":262},{"id":"1810.10147","title":"FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation","created":"2018-10-24","cited_by_count":254},{"id":"2105.11259","title":"PTR: Prompt Tuning with Rules for Text Classification","created":"2021-05-24","cited_by_count":238},{"id":"1511.08308","title":"Named Entity Recognition with Bidirectional LSTM-CNNs","created":"2015-11-26","cited_by_count":201},{"id":"1910.11476","title":"A Unified MRC Framework for Named Entity Recognition","created":"2019-10-25","cited_by_count":199},{"id":"2101.05779","title":"Structured Prediction as Translation between Augmented Natural Languages","created":"2021-01-14","cited_by_count":183},{"id":"2104.07650","title":"KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction","created":"2021-04-15","cited_by_count":180},{"id":"2004.13625","title":"Event Extraction by Answering (Almost) Natural Questions","created":"2020-04-28","cited_by_count":177},{"id":"2010.12309","title":"A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarios","created":"2020-10-23","cited_by_count":171},{"id":"2011.08641","title":"A Review of Generalized Zero-Shot Learning Methods","created":"2020-11-17","cited_by_count":159},{"id":"2104.05919","title":"Document-Level Event Argument Extraction by Conditional Generation","created":"2021-04-13","cited_by_count":155},{"id":"2106.01760","title":"Template-Based Named Entity Recognition Using BART","created":"2021-06-03","cited_by_count":153},{"id":"2105.07464","title":"Few-NERD: A Few-Shot Named Entity Recognition Dataset","created":"2021-05-16","cited_by_count":152},{"id":"2106.09232","title":"Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extraction","created":"2021-06-17","cited_by_count":129},{"id":"2004.13590","title":"MAVEN: A Massive General Domain Event Detection Dataset","created":"2020-04-28","cited_by_count":104},{"id":"2106.07499","title":"An Empirical Survey of Data Augmentation for Limited Data Learning in NLP","created":"2021-06-14","cited_by_count":101},{"id":"1905.05529","title":"Entity-Relation Extraction as Multi-Turn Question Answering","created":"2019-05-14","cited_by_count":96},{"id":"2004.14855","title":"TACRED Revisited: A Thorough Evaluation of the TACRED Relation Extraction Task","created":"2020-04-30","cited_by_count":96},{"id":"1910.07124","title":"FewRel 2.0: Towards More Challenging Few-Shot Relation Classification","created":"2019-10-16","cited_by_count":94},{"id":"2006.15509","title":"BOND: BERT-Assisted Open-Domain Named Entity Recognition with Distant Supervision","created":"2020-06-28","cited_by_count":91},{"id":"2010.05006","title":"Automated Concatenation of Embeddings for Structured Prediction","created":"2020-10-10","cited_by_count":86},{"id":"2108.12724","title":"DEGREE: A Data-Efficient Generation-Based Event Extraction Model","created":"2021-08-29","cited_by_count":83},{"id":"1911.03766","title":"Multi-Sentence Argument Linking","created":"2019-11-09","cited_by_count":81},{"id":"2109.13532","title":"Template-free Prompt Tuning for Few-shot NER","created":"2021-09-28","cited_by_count":73},{"id":"1707.01066","title":"Zero-Shot Transfer Learning for Event Extraction","created":"2017-07-04","cited_by_count":66},{"id":"1809.03599","title":"Learning Named Entity Tagger using Domain-Specific Dictionary","created":"2018-09-10","cited_by_count":65},{"id":"1903.01306","title":"Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks","created":"2019-03-04","cited_by_count":61},{"id":"2005.02472","title":"Cross-media Structured Common Space for Multimedia Event Extraction","created":"2020-05-05","cited_by_count":61},{"id":"1910.11621","title":"Meta-Learning with Dynamic-Memory-Based Prototypical Network for Few-Shot Event Detection","created":"2019-10-25","cited_by_count":54},{"id":"2112.08340","title":"GenIE: Generative Information Extraction","created":"2021-12-15","cited_by_count":53},{"id":"2109.06067","title":"Packed Levitated Marker for Entity and Relation Extraction","created":"2021-09-13","cited_by_count":49},{"id":"1911.10438","title":"CopyMTL: Copy Mechanism for Joint Extraction of Entities and Relations with Multi-Task Learning","created":"2019-11-24","cited_by_count":47},{"id":"2104.04697","title":"ZS-BERT: Towards Zero-Shot Relation Extraction with Attribute Representation Learning","created":"2021-04-10","cited_by_count":46},{"id":"2108.13655","title":"MELM: Data Augmentation with Masked Entity Language Modeling for Low-Resource NER","created":"2021-08-31","cited_by_count":42},{"id":"2007.02387","title":"Few-shot Relation Extraction via Bayesian Meta-learning on Relation Graphs","created":"2020-07-05","cited_by_count":39},{"id":"2110.08454","title":"Good Examples Make A Faster Learner: Simple Demonstration-based Learning for Low-resource NER","created":"2021-10-16","cited_by_count":38},{"id":"2105.14485","title":"CLEVE: Contrastive Pre-training for Event Extraction","created":"2021-05-30","cited_by_count":36},{"id":"1805.09927","title":"Robust Distant Supervision Relation Extraction via Deep Reinforcement Learning","created":"2018-05-24","cited_by_count":35},{"id":"2103.11647","title":"Prototypical Representation Learning for Relation Extraction","created":"2021-03-22","cited_by_count":33},{"id":"2109.06415","title":"Gradient Imitation Reinforcement Learning for Low Resource Relation Extraction","created":"2021-09-14","cited_by_count":33},{"id":"2010.01677","title":"Local Additivity Based Data Augmentation for Semi-supervised NER","created":"2020-10-04","cited_by_count":31},{"id":"2010.08210","title":"Coarse-to-Fine Pre-training for Named Entity Recognition","created":"2020-10-16","cited_by_count":28},{"id":"2104.08481","title":"Revisiting Few-shot Relation Classification: Evaluation Data and Classification Schemes","created":"2021-04-17","cited_by_count":27},{"id":"2109.00720","title":"LightNER: A Lightweight Tuning Paradigm for Low-resource NER via Pluggable Prompting","created":"2021-08-31","cited_by_count":27},{"id":"2110.07476","title":"Query and Extract: Refining Event Extraction as Type-oriented Binary Decoding","created":"2021-10-14","cited_by_count":26},{"id":"1909.02177","title":"NERO: A Neural Rule Grounding Framework for Label-Efficient Relation Extraction","created":"2019-09-05","cited_by_count":24},{"id":"1911.03091","title":"Relation Adversarial Network for Low Resource Knowledge Graph Completion","created":"2019-11-08","cited_by_count":24},{"id":"2201.11332","title":"Ontology-enhanced Prompt-tuning for Few-shot Learning","created":"2022-01-27","cited_by_count":24},{"id":"2105.10922","title":"OntoED: Low-resource Event Detection with Ontology Embedding","created":"2021-05-23","cited_by_count":23},{"id":"1812.06081","title":"A Neural Multi-Task Learning Framework to Jointly Model Medical Named Entity Recognition and Normalization","created":"2018-12-14","cited_by_count":20},{"id":"2012.02353","title":"Few-Shot Event Detection with Prototypical Amortized Conditional Random Field","created":"2020-12-04","cited_by_count":20},{"id":"1907.03228","title":"Zero-Shot Open Entity Typing as Type-Compatible Grounding","created":"2019-07-07","cited_by_count":19},{"id":"2109.12776","title":"Joint Multimedia Event Extraction from Video and Article","created":"2021-09-27","cited_by_count":19},{"id":"2010.16068","title":"Logic-guided Semantic Representation Learning for Zero-Shot Relation Classification","created":"2020-10-30","cited_by_count":18},{"id":"2201.03335","title":"DeepKE: A Deep Learning Based Knowledge Extraction Toolkit for Knowledge Base Population","created":"2022-01-10","cited_by_count":17},{"id":"2109.04108","title":"MapRE: An Effective Semantic Mapping Approach for Low-resource Relation Extraction","created":"2021-09-09","cited_by_count":16},{"id":"2009.02653","title":"A Survey on Machine Learning from Few Samples","created":"2020-09-06","cited_by_count":14},{"id":"1908.08788","title":"When Low Resource NLP Meets Unsupervised Language Model: Meta-pretraining Then Meta-learning for Few-shot Text Classification","created":"2019-08-22","cited_by_count":13},{"id":"2009.09841","title":"Finding Influential Instances for Distantly Supervised Relation Extraction","created":"2020-09-17","cited_by_count":13},{"id":"2105.09509","title":"Adaptive Knowledge-Enhanced Bayesian Meta-Learning for Few-shot Event Detection","created":"2021-05-20","cited_by_count":13},{"id":"2109.12383","title":"Language Model Priming for Cross-Lingual Event Extraction","created":"2021-09-25","cited_by_count":13},{"id":"2004.01267","title":"MZET: Memory Augmented Zero-Shot Fine-grained Named Entity Typing","created":"2020-04-02","cited_by_count":12},{"id":"2107.02126","title":"A Survey on Deep Learning Event Extraction: Approaches and Applications","created":"2021-07-05","cited_by_count":12},{"id":"2101.01907","title":"Deep Neural Network Based Relation Extraction: An Overview","created":"2021-01-06","cited_by_count":11},{"id":"2110.00479","title":"Learning to Ask for Data-Efficient Event Argument Extraction","created":"2021-10-01","cited_by_count":5},{"id":"2108.11635","title":"MCML: A Novel Memory-based Contrastive Meta-Learning Method for Few Shot Slot Tagging","created":"2021-08-26","cited_by_count":4}],"next_cursor":null}